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Becoming better at data science every day

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**Learning Philosophy**:
- The Power of Tiny Gains
- Master Adjacent Disciplines
- T-shaped skills
- Data Scientists Should Be More End-to-End
- Just in Time Learning

- Have basic business understanding
- Be able to frame an ML problem
- Be familiar with data ethics
- Be able to import data from multiple sources
- Be able to setup data annotation efficiently
- Be able to manipulate data with Numpy
- Be able to manipulate data with Pandas
- Be able to manipulate data in spreadsheets
- Be able to manipulate data in databases
- Be able to use Linux tools
- Be able to perform feature engineering
- Be able to experiment in a notebook
- Be able to visualize data
- Be able to do literature review using research papers
- Be able to model problems mathematically
- Be able to structure machine learning projects
- Be able to version control code
- Be able to version control data
- Be familiar with fundamentals of ML and DL
- Be able to implement models in scikit-learn
- Be able to implement models in Tensorflow and Keras
- Be able to implement models in PyTorch
- Be able to implement models using cloud services
- Be able to apply unsupervised learning algorithms
- Be able to implement NLP models
- Be familiar with Recommendation Systems
- Be able to implement computer vision models
- Be able to model graphs and network data
- Be able to implement models for timeseries and forecasting
- Be familiar with Reinforcement Learning
- Be able to optimize performance metric
- Be familiar with literature on model interpretability
- Be able to optimize model size for production
- Be able to write unit tests
- Be able to serve models via APIs
- Be able to build interactive UI for models
- Be able to deploy model to production
- Be able to perform load testing
- Be able to perform A/B testing
- Be proficient in Python
- Be familiar with compiled languages
- Have a general understanding of other parts of the stack
- Be familiar with fundamental Computer Science concepts
- Be able to apply proper software engineering process
- Be able to efficiently use a text editor
- Be able to communicate and collaborate well
- Be familiar with the hiring pipeline
- Broaden Perspective

- [X] Book: Delivering Happiness
- [X] Book: Good to Great: Why Some Companies Make the Leap...And Others Don't
- [X] Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
- [X] Book: How Google Works
- [X] Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business
- [X] Book: Rework
- [X] Book: The Airbnb Story
- [X] Book: The Personal MBA
- [X] Facebook: Digital marketing: get started
- [X] Facebook: Digital marketing: go further
- [X] Google Analytics for Beginners
- [X] Moz: The Beginner's Guide to SEO
- [X] Smartly: Marketing Fundamentals
- [X] Treehouse: SEO Basics
- [ ] Udacity: App Monetization
- [ ] Udacity: App Marketing
- [ ] Udacity: Get Your Startup Started
- [ ] Udacity: How to Build a Startup
- [ ] Youtube: SEO Unlocked
- [X] Welcome to the SEO Unlocked
0:10:09

- [X] Introduction to SEO and Why It's Important
0:10:29

- [ ] Keyword Research Part 1
0:19:20

- [ ] Keyword Research Part 2
0:09:56

- [ ] On-page and technical SEO Part 1
0:22:58

- [ ] On-page and technical SEO Part 2
0:12:16

- [ ] Mastering Technical SEO Audits
0:16:35

- [ ] Content Marketing Part 1
0:24:09

- [ ] Advanced Content Marketing Tactics
0:09:54

- [ ] The 10 Commandments of Content Marketing
0:19:01

- [ ] How to Edit Your Content For SEO
0:10:59

- [ ] Discover Your Competitive Strategy
0:09:12

- [ ] Over 4 Million Backlinks Built With This Simple Process
0:11:09

- [ ] How to Get POWERFUL Backlinks for Faster Rankings
0:09:40

- [ ] Get THOUSANDS of Backlinks On Semi-Autopilot
0:06:32

- [ ] How To Get The Most Out Of Google Analytics
0:07:45

- [ ] How to Setup Google Search Console
0:09:21

- [ ] How to Use Advanced Features in Google Analytics
0:10:52

- [ ] A Deep Dive Into Branding, Data & Experience
0:14:03

- [ ] How To Create A Compelling Brand
0:05:52

- [ ] Designing Your Customer Experience & Case Studies
0:07:32

- [X] Welcome to the SEO Unlocked

- [X] AWS: Types of Machine Learning Solutions
- [X] Article: Apply Machine Learning to your Business
- [X] Article: Resilience and Vibrancy: The 2020 Data & AI Landscape
- [X] Article: Software 2.0
- [X] Article: How Facebook uses super-efficient AI models to detect hate speech
- [X] Article: Highlights from ICML 2020
- [ ] Article: Recent Advances in Google Translate
- [ ] Article: A Peek at Trends in Machine Learning
- [ ] Article: How to deliver on Machine Learning projects
- [ ] Article: Data Science as a Product
- [X] Article: Customer service is full of machine learning problems
- [X] Article: Choosing Problems in Data Science and Machine Learning
- [X] Article: Why finance is deploying natural language processing
- [X] Article: Cannes: How ML saves us $1.7M a year on document previews
- [ ] Article: Machine Learning @ Monzo in 2020
- [X] Book: AI Superpowers: China, Silicon Valley, and the New World Order
- [X] Book: A Human's Guide to Machine Intelligence
- [X] Book: The Future Computed
- [ ] Book: Machine Learning Yearning by Andrew Ng
- [X] Book: Prediction Machines: The Simple Economics of Artificial Intelligence
- [ ] Book: Building Machine Learning Powered Applications: Going from Idea to Product
- [X] Coursera: AI For Everyone
- [ ] Datacamp: Case Studies in Statistical Thinking
- [X] Datacamp: Data Science for Everyone
- [X] Datacamp: Machine Learning with the Experts: School Budgets
- [X] Datacamp: Machine Learning for Everyone
- [X] Datacamp: Analyzing Police Activity with pandas
- [X] Datacamp: HR Analytics in Python: Predicting Employee Churn
- [X] Datacamp: Predicting Customer Churn in Python
- [X] Datacamp: Data Science for Managers
- [X] Facebook: Field Guide to Machine Learning
- [X] Google: Introduction to Machine Learning Problem Framing
- [X] Pluralsight: How to Think About Machine Learning Algorithms
- [ ] State of AI Report 2020
- [ ] Udacity: Problem Solving with Advanced Analytics
- [X] Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
- [X] Youtube: Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2
- [X] Youtube: How does YouTube recommend videos? - AI EXPLAINED!
0:33:53

- [X] Youtube: How does Google Translate's AI work?
0:15:02

- [X] Youtube: Data Science in Finance
0:17:52

- [X] Youtube: The Age of AI
- [X] How Far is Too Far? | The Age of A.I.
0:34:39

- [X] Healed through A.I. | The Age of A.I.
0:39:55

- [X] Using A.I. to build a better human | The Age of A.I.
0:44:27

- [X] Love, art and stories: decoded | The Age of A.I.
0:38:57

- [X] The 'Space Architects' of Mars | The Age of A.I.
0:30:10

- [X] Will a robot take my job? | The Age of A.I.
0:36:14

- [X] Saving the world one algorithm at a time | The Age of A.I.
0:46:37

- [X] How A.I. is searching for Aliens | The Age of A.I.
0:36:12

- [X] How Far is Too Far? | The Age of A.I.
- [ ] Youtube: Gradient Dissent Podcast
- [ ] DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery
0:55:11

- [X] ML Research and Production Pipelines with Chip Huyen
0:43:07

- [ ] Product Management for AI with Peter Skomoroch
1:28:14

- [X] Slow down and change one thing at a time - Advancing AI research with Josh Tobin
0:48:19

- [ ] Societal Impacts of Artificial Intelligence with Miles Brundage
1:02:25

- [ ] Deep Reinforcement Learning and Robotics with Peter Welinder
0:54:22

- [X] Machine learning across industries with Vicki Boykis
0:34:02

- [ ] Designing ML models for millions of consumer robots - Angela Bassa and Danielle Dean
0:52:38

- [ ] Building trustworthy AI systems and combating potential malicious use – A conversation w/ Jack Clark
0:55:56

- [X] Rachael Tatman - Conversational A.I. and Linguistics
0:36:51

- [X] Nicolas Koumchatzky - Machine Learning in Production for Self Driving Cars
0:44:56

- [X] Brandon Rohrer - Machine Learning in Production for Robots
0:34:31

- [ ] DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery
- [X] Youtube: Accuracy as a Failure
- [X] Youtube: Using Intent Data to Optimize the Self-Solve Experience
- [X] Youtube: Trillions of Questions, No Easy Answers: A (home) movie about how Google Search works
- [X] Youtube: Hugging Face, Transformers | NLP Research and Open Source | Interview with Julien Chaumond
- [X] Youtube: Vincent Warmerdam - Playing by the Rules-Based-Systems | PyData Eindhoven 2020
- [X] Youtube: Google Machine Learning System Design Mock Interview
- [X] Youtube: Netflix Machine Learning Mock Interview: Type-ahead Search
- [X] Youtube: Machine Learning design: Search engine for Q&A
- [X] Youtube: Engineering Systems for Real-Time Predictions @DoorDash

- [X] Article: How to Detect Bias in AI
- [X] Netflix: Coded Bias
- [X] Netflix: The Great Hack
- [X] Netflix: The Social Dilemma
- [ ] Practical Data Ethics
- [X] Lesson 1: Disinformation
- [ ] Lesson 2: Bias & Fairness
- [ ] Lesson 3: Ethical Foundations & Practical Tools
- [ ] Lesson 4: Privacy and surveillance
- [ ] Lesson 4 continued: Privacy and surveillance
- [ ] Lesson 5.1: The problem with metrics
- [ ] Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth
- [ ] Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib
- [ ] Lesson 6: Algorithmic Colonialism, and Next Steps

- [X] Docs: Beautiful Soup Documentation
- [X] Datacamp: Importing Data in Python (Part 2)
- [ ] Datacamp: Web Scraping in Python

- [X] Article: Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”
- [X] Article: We need Synthetic Data
- [ ] Article: Weak Supervision for Online Discussions
- [X] Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
- [X] Youtube: Training a NER Model with Prodigy and Transfer Learning
- [X] Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning
- [X] Youtube: ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations

- [X] Article: A Visual Intro to NumPy and Data Representation
- [ ] Article: NumPy Illustrated: The Visual Guide to NumPy
- [ ] Article: NumPy Fundamentals for Data Science and Machine Learning
- [X] Datacamp: Intro to Python for Data Science
- [X] Pluralsight: Working with Multidimensional Data Using NumPy

- [X] Article: Visualizing Pandas' Pivoting and Reshaping Functions
- [X] Article: A Gentle Visual Intro to Data Analysis in Python Using Pandas
- [ ] Article: Comprehensive Guide to Grouping and Aggregating with Pandas
- [X] Article: 8 Python Pandas Value_counts() tricks that make your work more efficient
- [X] Datacamp: pandas Foundations
- [X] Datacamp: Pandas Joins for Spreadsheet Users
- [X] Datacamp: Manipulating DataFrames with pandas
- [X] Datacamp: Merging DataFrames with pandas
- [X] Datacamp: Data Manipulation with pandas
- [X] Datacamp: Optimizing Python Code with pandas
- [X] Datacamp: Streamlined Data Ingestion with pandas
- [X] Datacamp: Analyzing Marketing Campaigns with pandas
- [X] edX: Implementing Predictive Analytics with Spark in Azure HDInsight
- [ ] Article: Modern Pandas

- [X] Datacamp: Spreadsheet basics
- [ ] Datacamp: Data Analysis with Spreadsheets
- [ ] Datacamp: Intermediate Spreadsheets for Data Science
- [ ] Datacamp: Pivot Tables with Spreadsheets
- [ ] Datacamp: Data Visualization in Spreadsheets
- [ ] Datacamp: Introduction to Statistics in Spreadsheets
- [ ] Datacamp: Conditional Formatting in Spreadsheets
- [ ] Datacamp: Marketing Analytics in Spreadsheets
- [ ] Datacamp: Error and Uncertainty in Spreadsheets
- [X] edX: Analyzing and Visualizing Data with Excel

- [X] Codecademy: SQL Track
- [X] Datacamp: Intro to SQL for Data Science
- [ ] Datacamp: Introduction to MongoDB in Python
- [ ] Datacamp: Intermediate SQL
- [ ] Datacamp: Exploratory Data Analysis in SQL
- [ ] Datacamp: Joining Data in PostgreSQL
- [X] Datacamp: Querying with TransactSQL
- [ ] Datacamp: Introduction to Databases in Python
- [ ] Datacamp: Reporting in SQL
- [ ] Datacamp: Applying SQL to Real-World Problems
- [ ] Datacamp: Analyzing Business Data in SQL
- [ ] Datacamp: Data-Driven Decision Making in SQL
- [ ] Datacamp: Database Design
- [ ] Udacity: SQL for Data Analysis
- [ ] Udacity: Intro to relational database
- [ ] Udacity: Database Systems Concepts & Design

- [ ] Article: Streamline your projects using Makefile
- [X] Calmcode: makefiles
- [X] Calmcode: entr
- [X] Codecademy: Learn the Command Line
- [X] Datacamp: Introduction to Shell for Data Science
- [X] Datacamp: Introduction to Bash Scripting
- [X] Datacamp: Data Processing in Shell
- [ ] LaunchSchool: Introduction to Commandline
- [ ] Learn Enough Command Line to be dangerous
- [ ] Thoughtbot: Mastering the Shell
- [ ] Thoughtbot: tmux
- [X] Udacity: Linux Command Line Basics
- [X] Udacity: Shell Workshop
- [ ] Web Bos: Command Line Power User
- [ ] Youtube: GNU Parallel

- [X] Article: Tips for Advanced Feature Engineering
- [ ] Article: Preparing data for a machine learning model
- [ ] Article: Feature selection for a machine learning model
- [ ] Article: Learning from imbalanced data
- [ ] Article: Hacker's Guide to Data Preparation for Machine Learning
- [ ] Article: Practical Guide to Handling Imbalanced Datasets
- [ ] Datacamp: Analyzing Social Media Data in Python
- [X] Datacamp: Dimensionality Reduction in Python
- [X] Datacamp: Preprocessing for Machine Learning in Python
- [X] Datacamp: Data Types for Data Science
- [X] Datacamp: Cleaning Data in Python
- [X] Datacamp: Feature Engineering for Machine Learning in Python
- [ ] Datacamp: Importing & Managing Financial Data in Python
- [ ] Datacamp: Manipulating Time Series Data in Python
- [ ] Datacamp: Working with Geospatial Data in Python
- [ ] Datacamp: Analyzing IoT Data in Python
- [ ] Datacamp: Dealing with Missing Data in Python
- [ ] Datacamp: Exploratory Data Analysis in Python
- [X] edX: Data Science Essentials
- [ ] Google: Feature Engineering
- [ ] Udacity: Creating an Analytical Dataset

- [X] Article: Securely storing configuration credentials in a Jupyter Notebook
- [X] Article: Automatically Reload Modules with %autoreload
- [ ] Calmcode: ipywidgets
- [X] Documentation: Jupyter Lab
- [X] Pluralsight: Getting Started with Jupyter Notebook and Python
- [X] Youtube: William Horton - A Brief History of Jupyter Notebooks
- [X] Youtube: I Like Notebooks
- [X] Youtube: I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
- [X] Youtube: Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
- [X] Youtube: nbdev live coding with Hamel Husain
- [X] Youtube: How to Use JupyterLab

- [X] Article: Creating a Catchier Word Cloud Presentation
- [X] Article: Effectively Using Matplotlib
- [ ] Datacamp: Introduction to Data Visualization with Python
- [X] Datacamp: Introduction to Seaborn
- [X] Datacamp: Introduction to Matplotlib
- [ ] Datacamp: Intermediate Data Visualization with Seaborn
- [ ] Datacamp: Visualizing Time Series Data in Python
- [ ] Datacamp: Improving Your Data Visualizations in Python
- [ ] Datacamp: Visualizing Geospatial Data in Python
- [ ] Datacamp: Interactive Data Visualization with Bokeh
- [ ] Udacity: Data Visualization in Tableau
- [ ] Youtube: Jake VanderPlas - Exploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018
- [ ] Youtube: Shantam Raj- Rapidly emulating professional visualizations from New York Times| PyData Global 2020
- [ ] UWData: Data Visualization Curriculum

- [X] Paper: A Neural Probabilistic Language Model
- [ ] Paper: Efficient Estimation of Word Representations in Vector Space
- [X] Paper: Sequence to Sequence Learning with Neural Networks
- [X] Paper: Neural Machine Translation by Jointly Learning to Align and Translate
- [X] Paper: Attention Is All You Need
- [X] Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- [X] Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
- [X] Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDF
- [ ] Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
- [ ] Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- [X] Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
- [X] Paper: Collaborative Filtering for Implicit Feedback Datasets
- [ ] Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
- [X] Paper: Factorization Machines
- [X] Paper: Wide & Deep Learning for Recommender Systems
- [X] Paper: Neural Factorization Machines for Sparse Predictive Analytics
- [X] Paper: Multiword Expressions: A Pain in the Neck for NLP
- [X] Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
- [X] Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
- [X] Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
- [X] Paper: A Simple Framework for Contrastive Learning of Visual Representations
- [X] Paper: Self-Supervised Learning of Pretext-Invariant Representations
- [X] Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- [X] Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning
- [ ] Paper: A Survey on Contextual Embeddings
- [X] Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
- [ ] Paper: Shortcut Learning in Deep Neural Networks
- [X] Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia
- [X] Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
- [X] Paper: Zero-shot Text Classification With Generative Language Models
- [X] Paper: How to Fine-Tune BERT for Text Classification?
- [X] Paper: Universal Sentence Encoder
- [X] Paper: Enriching Word Vectors with Subword Information
- [ ] Paper: Deep Learning Based Text Classification: A Comprehensive Review
- [X] Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
- [X] Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
- [X] Paper: Temporal Ensembling for Semi-Supervised Learning
- [X] Paper: Boosting Self-Supervised Learning via Knowledge Transfer
- [X] Paper: Follow-up Question Generation
- [X] Paper: The Hardware Lottery
- [X] Paper: Question Generation via Overgenerating Transformations and Ranking
- [X] Paper: Good Question! Statistical Ranking for Question Generation
- [ ] Paper: Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
- [ ] Paper: Interpolation Consistency Training for Semi-Supervised Learning
- [X] Paper: Neural Text Generation: A Practical Guide
- [ ] Paper: Pest Management In Cotton Farms: An AI-System Case Study from the Global South
- [ ] Paper: BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
- [ ] Paper: On the surprising similarities between supervised and self-supervised models
- [X] Paper: All-but-the-Top: Simple and Effective Postprocessing for Word Representations
- [X] Paper: Simple and Effective Dimensionality Reduction for Word Embeddings
- [X] Paper: A Survey on Visual Transformer
- [X] Youtube: mixup: Beyond Empirical Risk Minimization (Paper Explained)

- [ ] 3Blue1Brown: Essence of Calculus
- [ ] The Essence of Calculus, Chapter 1
0:17:04

- [ ] The paradox of the derivative | Essence of calculus, chapter 2
0:17:57

- [ ] Derivative formulas through geometry | Essence of calculus, chapter 3
0:18:43

- [ ] Visualizing the chain rule and product rule | Essence of calculus, chapter 4
0:16:52

- [ ] What's so special about Euler's number e? | Essence of calculus, chapter 5
0:13:50

- [ ] Implicit differentiation, what's going on here? | Essence of calculus, chapter 6
0:15:33

- [ ] Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7
0:18:26

- [ ] Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8
0:20:46

- [ ] What does area have to do with slope? | Essence of calculus, chapter 9
0:12:39

- [ ] Higher order derivatives | Essence of calculus, chapter 10
0:05:38

- [ ] Taylor series | Essence of calculus, chapter 11
0:22:19

- [ ] What they won't teach you in calculus
0:16:22

- [ ] The Essence of Calculus, Chapter 1
- [ ] 3Blue1Brown: Essence of linear algebra
- [ ] Vectors, what even are they? | Essence of linear algebra, chapter 1
0:09:52

- [ ] Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2
0:09:59

- [ ] Linear transformations and matrices | Essence of linear algebra, chapter 3
0:10:58

- [ ] Matrix multiplication as composition | Essence of linear algebra, chapter 4
0:10:03

- [ ] Three-dimensional linear transformations | Essence of linear algebra, chapter 5
0:04:46

- [ ] The determinant | Essence of linear algebra, chapter 6
0:10:03

- [ ] Inverse matrices, column space and null space | Essence of linear algebra, chapter 7
0:12:08

- [ ] Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8
0:04:27

- [ ] Dot products and duality | Essence of linear algebra, chapter 9
0:14:11

- [ ] Cross products | Essence of linear algebra, Chapter 10
0:08:53

- [ ] Cross products in the light of linear transformations | Essence of linear algebra chapter 11
0:13:10

- [ ] Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12
0:12:12

- [ ] Change of basis | Essence of linear algebra, chapter 13
0:12:50

- [ ] Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14
0:17:15

- [ ] Abstract vector spaces | Essence of linear algebra, chapter 15
0:16:46

- [ ] Vectors, what even are they? | Essence of linear algebra, chapter 1
- [X] 3Blue1Brown: Neural networks
- [X] Article: A Visual Tour of Backpropagation
- [X] Article: Entropy, Cross Entropy, and KL Divergence
- [ ] Article: Introduction to Linear Algebra for Applied Machine Learning with Python
- [ ] Article: Entropy of a probability distribution — in layman’s terms
- [ ] Article: KL Divergence — in layman’s terms
- [ ] Article: Probability Distributions
- [ ] Article: Relearning Matrices as Linear Functions
- [ ] Article: You Could Have Come Up With Eigenvectors - Here's How
- [ ] Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Search
- [ ] Article: Interactive Visualization of Why Eigenvectors Matter
- [ ] Article: Cross-Entropy and KL Divergence
- [ ] Article: Why Randomness Is Information?
- [ ] Article: Basic Probability Theory
- [X] Article: Math You Need to Succeed In ML Interviews
- [ ] Book: Basics of Linear Algebra for Machine Learning
- [X] Datacamp: Introduction to Statistics in Python
- [X] Datacamp: Foundations of Probability in Python
- [ ] Datacamp: Statistical Thinking in Python (Part 1)
- [ ] Datacamp: Statistical Thinking in Python (Part 2)
- [ ] Datacamp: Statistical Simulation in Python
- [X] edX: Essential Statistics for Data Analysis using Excel
- [ ] Computational Linear Algebra for Coders
- [ ] Khan Academy: Precalculus
- [ ] Khan Academy: Probability
- [ ] Khan Academy: Differential Calculus
- [ ] Khan Academy: Multivariable Calculus
- [ ] Khan Academy: Linear Algebra
- [ ] MIT: 18.06 Linear Algebra (Professor Strang)
- [X] 1. The Geometry of Linear Equations
0:39:49

- [X] 2. Elimination with Matrices.
0:47:41

- [X] 3. Multiplication and Inverse Matrices
0:46:48

- [X] 4. Factorization into A = LU
0:48:05

- [X] 5. Transposes, Permutations, Spaces R^n
0:47:41

- [X] 6. Column Space and Nullspace
0:46:01

- [X] 9. Independence, Basis, and Dimension
0:50:14

- [X] 10. The Four Fundamental Subspaces
0:49:20

- [X] 11. Matrix Spaces; Rank 1; Small World Graphs
0:45:55

- [X] 14. Orthogonal Vectors and Subspaces
0:49:47

- [X] 15. Projections onto Subspaces
0:48:51

- [X] 16. Projection Matrices and Least Squares
0:48:05

- [X] 17. Orthogonal Matrices and Gram-Schmidt
0:49:09

- [X] 21. Eigenvalues and Eigenvectors
0:51:22

- [ ] 22. Diagonalization and Powers of A
0:51:50

- [ ] 24. Markov Matrices; Fourier Series
0:51:11

- [ ] 25. Symmetric Matrices and Positive Definiteness
0:43:52

- [ ] 27. Positive Definite Matrices and Minima
0:50:40

- [ ] 29. Singular Value Decomposition
0:40:28

- [ ] 30. Linear Transformations and Their Matrices
0:49:27

- [ ] 31. Change of Basis; Image Compression
0:50:13

- [ ] 33. Left and Right Inverses; Pseudoinverse
0:41:52

- [X] 1. The Geometry of Linear Equations
- [ ] StatQuest: Statistics Fundamentals
- [ ] StatQuest: Histograms, Clearly Explained
0:03:42

- [ ] StatQuest: What is a statistical distribution?
0:05:14

- [ ] StatQuest: The Normal Distribution, Clearly Explained!!!
0:05:12

- [ ] Statistics Fundamentals: Population Parameters
0:14:31

- [ ] Statistics Fundamentals: The Mean, Variance and Standard Deviation
0:14:22

- [ ] StatQuest: What is a statistical model?
0:03:45

- [ ] StatQuest: Sampling A Distribution
0:03:48

- [ ] Hypothesis Testing and The Null Hypothesis
0:14:40

- [ ] Alternative Hypotheses: Main Ideas!!!
0:09:49

- [ ] p-values: What they are and how to interpret them
0:11:22

- [ ] How to calculate p-values
0:25:15

- [ ] p-hacking: What it is and how to avoid it!
0:13:44

- [ ] Statistical Power, Clearly Explained!!!
0:08:19

- [ ] Power Analysis, Clearly Explained!!!
0:16:44

- [ ] Covariance and Correlation Part 1: Covariance
0:22:23

- [ ] Covariance and Correlation Part 2: Pearson's Correlation
0:19:13

- [ ] StatQuest: R-squared explained
0:11:01

- [ ] The Central Limit Theorem
0:07:35

- [ ] StatQuickie: Standard Deviation vs Standard Error
0:02:52

- [ ] StatQuest: The standard error
0:11:43

- [ ] StatQuest: Technical and Biological Replicates
0:05:27

- [ ] StatQuest - Sample Size and Effective Sample Size, Clearly Explained
0:06:32

- [ ] Bar Charts Are Better than Pie Charts
0:01:45

- [ ] StatQuest: Boxplots, Clearly Explained
0:02:33

- [ ] StatQuest: Logs (logarithms), clearly explained
0:15:37

- [ ] StatQuest: Confidence Intervals
0:06:41

- [ ] StatQuickie: Thresholds for Significance
0:06:40

- [ ] StatQuickie: Which t test to use
0:05:10

- [ ] StatQuest: One or Two Tailed P-Values
0:07:05

- [ ] The Binomial Distribution and Test, Clearly Explained!!!
0:15:46

- [ ] StatQuest: Quantiles and Percentiles, Clearly Explained!!!
0:06:30

- [ ] StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained
0:06:55

- [ ] StatQuest: Quantile Normalization
0:04:51

- [X] StatQuest: Probability vs Likelihood
0:05:01

- [ ] StatQuest: Maximum Likelihood, clearly explained!!!
0:06:12

- [ ] Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0
0:09:39

- [ ] Why Dividing By N Underestimates the Variance
0:17:14

- [ ] Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!
0:11:24

- [ ] Maximum Likelihood For the Normal Distribution, step-by-step!
0:19:50

- [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!!
0:11:30

- [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
0:16:20

- [ ] Live 2020-04-20!!! Expected Values
0:33:00

- [ ] StatQuest: Histograms, Clearly Explained
- [ ] Udacity: Algebra Review
- [ ] Udacity: Differential Equations in Action
- [X] Udacity: Eigenvectors and Eigenvalues
- [ ] Udacity: Linear Algebra Refresher
- [ ] Udacity: Statistics
- [ ] Udacity: Intro to Descriptive Statistics
- [ ] Udacity: Intro to Inferential Statistics
- [ ] Youtube: Visualizing Deep Learning

- [X] Article: I trained a model. What is next?
- [X] Article: pydantic
- [ ] Article: Always start with a stupid model, no exceptions.
- [ ] Article: Organizing machine learning projects: project management guidelines
- [ ] Article: Most impactful AI trends of 2018: the rise of ML Engineering
- [ ] Article: Building machine learning products: a problem well-defined is a problem half-solved.
- [X] Article: Simple considerations for simple people building fancy neural networks
- [ ] Article: Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application
- [ ] Article: How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
- [ ] Article: Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
- [ ] Article: Deep Learning in Production: Laptop set up and system design
- [X] Article: Configuring Google Colab Like A Pro
- [X] Article: Stop using print, start using loguru in Python
- [ ] Coursera: Structuring Machine Learning Projects
- [X] Datacamp: Conda Essentials
- [ ] Datacamp: Conda for Building & Distributing Packages
- [X] Datacamp: Creating Robust Python Workflows
- [X] Datacamp: Software Engineering for Data Scientists in Python
- [X] Datacamp: Designing Machine Learning Workflows in Python
- [X] Datacamp: Object-Oriented Programming in Python
- [ ] Datacamp: Command Line Automation in Python
- [X] Datacamp: Introduction to Data Engineering
- [ ] Datacamp: Experimental Design in Python
- [X] Developing Python Packages
- [X] Full Stack Deep Learning Bootcamp: March 2019
- [X] Lecture 1: Introduction to Deep Learning
- [X] Lecture 2: Setting Up Machine Learning Projects
- [X] Lecture 3: Introduction to the Text Recognizer Project
- [X] Lecture 4: Infrastructure and Tooling
- [X] Lecture 5: Tracking Experiments
- [X] Lecture 6: Data Management
- [X] Lecture 7: Machine Learning Teams
- [X] Lecture 9: Lukas Biewald
- [X] Lecture 10: Troubleshooting Deep Neural Networks
- [X] Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment
- [X] Lecture 12: Testing and Deployment
- [X] Lecture 13: Research Directions
- [X] Lecture 14: Jeremy Howard
- [X] Lecture 15: Richard Socher
- [X] Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning

- [ ] MIT: The Missing Semester of CS Education
- [ ] Lecture 1: Course Overview + The Shell (2020)
0:48:16

- [ ] Lecture 2: Shell Tools and Scripting (2020)
0:48:55

- [ ] Lecture 3: Editors (vim) (2020)
0:48:26

- [ ] Lecture 4: Data Wrangling (2020)
0:50:03

- [ ] Lecture 5: Command-line Environment (2020)
0:56:06

- [ ] Lecture 6: Version Control (git) (2020)
1:24:59

- [ ] Lecture 7: Debugging and Profiling (2020)
0:54:13

- [ ] Lecture 8: Metaprogramming (2020)
0:49:52

- [ ] Lecture 9: Security and Cryptography (2020)
1:00:59

- [ ] Lecture 10: Potpourri (2020)
0:57:54

- [ ] Lecture 11: Q&A (2020)
0:53:52

- [ ] Lecture 1: Course Overview + The Shell (2020)
- [X] Treehouse: Object Oriented Python
- [X] Treehouse: Setup Local Python Environment
- [ ] Udacity: Writing READMEs
- [X] Youtube: Weights and Biases Tutorial
- [X] Youtube: Integrate Weights & Biases with PyTorch
- [X] Youtube: Log (Almost) Anything with Weights & Biases
- [ ] Youtube: MLOps Tutorials
- [ ] MLOps Tutorial #1: Intro to Continuous Integration for ML
17:44:00

- [ ] MLOps Tutorial #2: When data is too big for Git
10:51:00

- [ ] MLOps Tutorial #3: Track ML models with Git & GitHub Actions
14:12:00

- [ ] MLOps Tutorial #1: Intro to Continuous Integration for ML
- [X] Youtube: Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16
- [ ] Youtube: OO Design and Testing Patterns for Machine Learning with Chris Gerpheide
- [ ] Youtube: MLSys Seminars Fall 2020
- [X] Stanford MLSys Seminar Episode 0: ML + Systems
0:11:49

- [ ] Stanford MLSys Seminar Episode 1: Marco Tulio Ribeiro
1:00:38

- [ ] Stanford MLSys Seminar Episode 2: Matei Zaharia
0:59:44

- [ ] Stanford MLSys Seminar Episode 3: Virginia Smith
1:00:55

- [ ] Stanford MLSys Seminar Episode 4: Alex Ratner
1:13:34

- [X] Stanford MLSys Seminar Episode 5: Chip Huyen
1:06:44

- [X] Stanford MLSys Seminar Episode 0: ML + Systems

- [ ] Article: Mastering Git Stash Workflow
- [X] Article: How to Become a Master of Git Tags
- [X] Article: How to track large files in Github / Bitbucket? Git LFS to the rescue
- [ ] Article: Keep your git directory clean with
git clean

andgit trash

- [X] Codecademy: Learn Git
- [X] Code School: Git Real
- [X] Datacamp: Introduction to Git for Data Science
- [ ] Learn enough git to be dangerous
- [ ] Learn Git Branching
- [ ] Thoughtbot: Mastering Git
- [X] Udacity: GitHub & Collaboration
- [X] Udacity: How to Use Git and GitHub
- [X] Udacity: Version Control with Git
- [X] Youtube: 045 Introduction to Git LFS

- [X] Article: Validating your Machine Learning Model
- [X] Article: One-vs-Rest strategy for Multi-Class Classification
- [X] Article: Multi-class Classification — One-vs-All & One-vs-One
- [X] Article: One-vs-Rest and One-vs-One for Multi-Class Classification
- [ ] Article: The Complete Guide to AUC and Average Precision: Simulations and Visualizations
- [ ] Article: Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks
- [X] Article: Best Use of Train/Val/Test Splits, with Tips for Medical Data
- [ ] Article: Measuring Performance: AUPRC and Average Precision
- [ ] Article: Measuring Performance: AUC (AUROC)
- [ ] Article: Measuring Performance: The Confusion Matrix
- [ ] Article: Measuring Performance: Accuracy
- [ ] Article: Naive Bayes classification
- [ ] Article: Linear regression
- [ ] Article: Polynomial regression
- [ ] Article: Logistic regression
- [X] Article: Decision trees
- [ ] Article: K-nearest neighbors
- [X] Article: Support Vector Machines
- [ ] Article: Random forests
- [ ] Article: Boosted trees
- [ ] Article: ROC Curves: Intuition Through Visualization
- [ ] Article: Hacker's Guide to Fundamental Machine Learning Algorithms with Python
- [X] Article: Simple Ways to Tackle Class Imbalance
- [ ] Article: MLE and MAP — in layman’s terms
- [X] Article: The Last 5 Years In Deep Learning
- [ ] Article: Visualizing Optimization Trajectory of Neural Nets
- [ ] An overview of gradient descent optimization algorithms
- [ ] Article: Optimization for Deep Learning Highlights in 2017
- [ ] Article: Neural networks: activation functions
- [ ] Article: Neural networks: training with backpropagation
- [ ] Article: Gradient descent
- [ ] Article: Setting the learning rate of your neural network
- [ ] Article: Deep neural networks: preventing overfitting
- [ ] Article: Normalizing your data (specifically, input and batch normalization)
- [ ] Article: Batch Normalization
- [ ] Article: Are Deep Neural Networks Dramatically Overfitted?
- [ ] Article: Attention? Attention!
- [ ] Article: How to Explain the Prediction of a Machine Learning Model?
- [ ] Article: Neural Network from scratch-part 1
- [ ] Article: Neural Network from scratch-part 2
- [ ] Article: Deep Learning Algorithms - The Complete Guide
- [ ] Article: In-layer normalization techniques for training very deep neural networks
- [ ] Article: Cross-entropy for classification
- [ ] Article: Perceptron to Deep-Neural-Network
- [ ] Article: Dismantling Neural Networks to Understand the Inner Workings with Math and Pytorch
- [ ] Article: Label Smoothing Explained using Microsoft Excel
- [X] Article: Precision, Recall, Accuracy, and F1 Score for Multi-Label Classification
- [X] AWS: The Elements of Data Science
- [X] AWS: Understanding Neural Networks
- [ ] Book: Approaching (Almost) Any Machine Learning Problem
- [ ] Book: Pattern Recognition and Machine Learning
- [ ] Book: Grokking Deep Learning
- [ ] Book: Make Your Own Neural Network
- [ ] Coursera: Neural Networks and Deep Learning
- [X] Datacamp: AI Fundamentals
- [ ] Datacamp: Extreme Gradient Boosting with XGBoost
- [X] Datacamp: Ensemble Methods in Python
- [ ] Datacamp: Foundations of Predictive Analytics in Python (Part 1)
- [ ] Datacamp: Foundations of Predictive Analytics in Python (Part 2)
- [X] Elements of AI
- [X] edX: Principles of Machine Learning
- [X] edX: Data Science Essentials
- [ ] Fast.ai: Deep Learning for Coder (2020)
- [X] Pluralsight: Deep Learning: The Big Picture
- [ ] StatQuest: Machine Learning
- [X] A Gentle Introduction to Machine Learning
0:12:45

- [X] Machine Learning Fundamentals: Cross Validation
0:06:04

- [X] Machine Learning Fundamentals: The Confusion Matrix
0:07:12

- [X] Machine Learning Fundamentals: Sensitivity and Specificity
0:11:46

- [X] Machine Learning Fundamentals: Bias and Variance
0:06:36

- [X] ROC and AUC, Clearly Explained!
0:16:26

- [X] StatQuest: Fitting a line to data, aka least squares, aka linear regression.
0:09:21

- [X] StatQuest: Linear Models Pt.1 - Linear Regression
0:27:26

- [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!!
0:11:30

- [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
0:16:20

- [ ] StatQuest: Logistic Regression
0:08:47

- [ ] Logistic Regression Details Pt1: Coefficients
0:19:02

- [ ] Logistic Regression Details Pt 2: Maximum Likelihood
0:10:23

- [ ] Logistic Regression Details Pt 3: R-squared and p-value
0:15:25

- [ ] Saturated Models and Deviance
0:18:39

- [ ] Deviance Residuals
0:06:18

- [ ] Regularization Part 1: Ridge (L2) Regression
0:20:26

- [ ] Regularization Part 2: Lasso (L1) Regression
0:08:19

- [ ] Ridge vs Lasso Regression, Visualized!!!
0:09:05

- [ ] Regularization Part 3: Elastic Net Regression
0:05:19

- [ ] StatQuest: Principal Component Analysis (PCA), Step-by-Step
0:21:57

- [ ] StatQuest: PCA main ideas in only 5 minutes!!!
0:06:04

- [ ] StatQuest: PCA - Practical Tips
0:08:19

- [ ] StatQuest: PCA in Python
0:11:37

- [ ] StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
0:15:12

- [ ] StatQuest: MDS and PCoA
0:08:18

- [ ] StatQuest: t-SNE, Clearly Explained
0:11:47

- [X] StatQuest: Hierarchical Clustering
0:11:19

- [ ] StatQuest: K-means clustering
0:08:57

- [X] StatQuest: K-nearest neighbors, Clearly Explained
0:05:30

- [X] Naive Bayes, Clearly Explained!!!
0:15:12

- [X] Gaussian Naive Bayes, Clearly Explained!!!
0:09:41

- [X] StatQuest: Decision Trees
0:17:22

- [X] StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
0:05:16

- [X] Regression Trees, Clearly Explained!!!
0:22:33

- [X] How to Prune Regression Trees, Clearly Explained!!!
0:16:15

- [X] StatQuest: Random Forests Part 1 - Building, Using and Evaluating
0:09:54

- [ ] StatQuest: Random Forests Part 2: Missing data and clustering
0:11:53

- [ ] The Chain Rule
0:18:23

- [ ] Gradient Descent, Step-by-Step
0:23:54

- [ ] Stochastic Gradient Descent, Clearly Explained!!!
0:10:53

- [ ] AdaBoost, Clearly Explained
0:20:54

- [ ] Gradient Boost Part 1: Regression Main Ideas
0:15:52

- [ ] Gradient Boost Part 2: Regression Details
0:26:45

- [ ] Gradient Boost Part 3: Classification
0:17:02

- [ ] Gradient Boost Part 4: Classification Details
0:36:59

- [X] Support Vector Machines, Clearly Explained!!!
0:20:32

- [X] Support Vector Machines Part 2: The Polynomial Kernel
0:07:15

- [X] Support Vector Machines Part 3: The Radial (RBF) Kernel
0:15:52

- [ ] XGBoost Part 1: Regression
0:25:46

- [ ] XGBoost Part 2: Classification
0:25:17

- [ ] XGBoost Part 3: Mathematical Details
0:27:24

- [ ] XGBoost Part 4: Crazy Cool Optimizations
0:24:27

- [ ] StatQuest: Fiitting a curve to data, aka lowess, aka loess
0:10:10

- [ ] Statistics Fundamentals: Population Parameters
0:14:31

- [ ] Principal Component Analysis (PCA) clearly explained (2015)
0:20:16

- [ ] Decision Trees in Python from Start to Finish
1:06:23

- [X] A Gentle Introduction to Machine Learning
- [ ] Udacity: Deep Learning
- [ ] Udacity: A Friendly Introduction to Machine Learning
- [ ] Udacity: Intro to Data Analysis
- [ ] Udacity: Intro to Data Science
- [ ] Udacity: Intro to Machine Learning
- [ ] Udacity: Classification Models
- [ ] Youtube: Applied Machine Learning 2020
- [X] Channel Intro - Applied Machine Learning
0:01:28

- [ ] Applied ML 2020 - 01 Introduction
1:16:01

- [X] Applied ML 2020 - 02 Visualization and matplotlib
1:07:30

- [X] Applied ML 2020 - 03 Supervised learning and model validation
1:12:00

- [X] Applied ML 2020 - 04 - Preprocessing
1:07:40

- [ ] Applied ML 2020 - 05 - Linear Models for Regression
1:06:54

- [ ] Applied ML 2020 - 06 - Linear Models for Classification
1:07:50

- [X] Applied ML 2020 - 07 - Decision Trees and Random Forests
1:07:58

- [X] Applied ML 2020 - 08 - Gradient Boosting
1:02:12

- [X] Applied ML 2020 - 09 - Model Evaluation and Metrics
1:18:23

- [X] Applied ML 2020 - 10 - Calibration, Imbalanced data
1:16:14

- [X] Applied ML 2020 - 11 - Model Inspection and Feature Selection
1:15:15

- [ ] Applied ML 2020 - 12 - AutoML (plus some feature selection)
1:25:38

- [ ] Applied ML 2020 - 13 - Dimensionality reduction
1:30:34

- [X] Applied ML 2020 - 14 - Clustering and Mixture Models
1:26:33

- [X] Applied ML 2020 - 15 - Working with Text Data
1:27:08

- [X] Applied ML 2020 - 16 - Topic models for text data
1:18:34

- [ ] Applied ML 2020 - 17 - Word vectors and document embeddings
1:03:04

- [ ] Applied ML 2020 - 18 - Neural Networks
1:19:36

- [ ] Applied ML 2020 - 21 - Time Series and Forecasting
1:12:36

- [X] Channel Intro - Applied Machine Learning
- [ ] Youtube: Neural Networks from Scratch in Python
- [ ] Neural Networks from Scratch - P.1 Intro and Neuron Code
0:16:59

- [ ] Neural Networks from Scratch - P.2 Coding a Layer
0:15:06

- [ ] Neural Networks from Scratch - P.3 The Dot Product
0:25:17

- [ ] Neural Networks from Scratch - P.4 Batches, Layers, and Objects
0:33:46

- [ ] Neural Networks from Scratch - P.5 Hidden Layer Activation Functions
0:40:05

- [ ] Neural Networks from Scratch - P.1 Intro and Neuron Code
- [ ] Youtube: Visualizing Deep Learning
- [X] Youtube: Deep Double Descent
- [X] Youtube: How do we check if a neural network has learned a specific phenomenon?
- [X] Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation

- [X] Article: Stacking made easy with Sklearn
- [X] Article: A Guide to Calibration Plots in Python
- [X] Calmcode: human-learn
- [X] Datacamp: Supervised Learning with scikit-learn
- [X] Datacamp: Machine Learning with Tree-Based Models in Python
- [ ] Datacamp: Introduction to Linear Modeling in Python
- [X] Datacamp: Linear Classifiers in Python
- [ ] Datacamp: Generalized Linear Models in Python
- [X] Notebook: scikit-learn tips
- [X] Pluralsight: Building Machine Learning Models in Python with scikit-learn
- [ ] Video: human learn
- [X] Youtube: dabl: Automatic Machine Learning with a Human in the Loop
00:25:43

- [X] Youtube: Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python

- [X] Coursera: Introduction to Tensorflow
- [X] Coursera: Convolutional Neural Networks in TensorFlow
- [ ] Coursera: Getting Started With Tensorflow 2
- [ ] Coursera: Customising your models with TensorFlow 2
- [X] Deeplizard: Keras - Python Deep Learning Neural Network API
- [ ] Book: Deep Learning with Python (Page: 276)
- [X] Datacamp: Deep Learning in Python
- [X] Datacamp: Convolutional Neural Networks for Image Processing
- [X] Datacamp: Introduction to TensorFlow in Python
- [X] Datacamp: Introduction to Deep Learning with Keras
- [X] Datacamp: Advanced Deep Learning with Keras
- [ ] Google: Intro to Tensorflow
- [ ] Google: Machine Learning Crash Course
- [X] Pluralsight: Deep Learning with Keras
- [X] Udacity: Intro to TensorFlow for Deep Learning

- [X] Article: Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups
- [X] Article: An introduction to PyTorch Lightning with comparisons to PyTorch
- [X] Article: Scaling Logistic Regression Via Multi-GPU/TPU Training
- [X] Article: PyTorch Lightning Bolts — From Linear, Logistic Regression on TPUs to pre-trained GANs
- [ ] Article: Keeping Up with PyTorch Lightning and Hydra
- [X] Article: PyTorch Lightning 0.9 — synced BatchNorm, DataModules and final API!
- [X] Article: PyTorch Lightning: Metrics
- [X] Article: PyTorch Multi-GPU Metrics Library and More in PyTorch Lightning 0.8.1
- [X] Article: 7 Tips To Maximize PyTorch Performance
- [X] Article: From PyTorch to PyTorch Lightning — A gentle introduction
- [X] Article: Converting From Keras To PyTorch Lightning
- [X] Article: How Wadhwani AI Uses PyTorch To Empower Cotton Farmers
- [X] Article: PyTorch Loss Functions: The Ultimate Guide
- [X] Article: Supercharge your Training with Pytorch Lightning + Weights & Biases
- [X] Article: Sharded: A New Technique To Double The Size Of PyTorch Models
- [X] Article: Introducing PyTorch Lightning Sharded: Train SOTA Models, With Half The Memory
- [X] Article: Transform your ML-model to Pytorch with Hummingbird
- [ ] Article: Distributed model training in PyTorch using DistributedDataParallel
- [X] Article: Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health
- [X] Article: Pad pack sequences for Pytorch batch processing with DataLoader
- [X] Article: Understanding Bidirectional RNN in PyTorch
- [ ] Article: PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA
- [ ] Article: But what are PyTorch DataLoaders really?
- [ ] Article: How to Build a Streaming DataLoader with PyTorch
- [ ] Article: Tricks for training PyTorch models to convergence more quickly
- [ ] Article: The One PyTorch Trick Which You Should Know
- [ ] Article: A developer-friendly guide to model pruning in PyTorch
- [ ] Article: A developer-friendly guide to model quantization with PyTorch
- [ ] Article: Distributed model training in PyTorch using DistributedDataParallel
- [ ] Article: A developer-friendly guide to mixed precision training with PyTorch
- [ ] Article: Fit More and Train Faster With ZeRO via DeepSpeed and FairScale
- [ ] Article: Faster Deep Learning Training with PyTorch – a 2021 Guide
- [ ] Article: EINSUM IS ALL YOU NEED - EINSTEIN SUMMATION IN DEEP LEARNING
- [X] Article: Using PyTorch + NumPy? You're making a mistake.
- [ ] Notebook: Tensor Arithmetic
- [ ] Notebook: Autograd
- [ ] Notebook: Optimization
- [ ] Notebook: Network modules
- [ ] Notebook: Datasets and Dataloaders
- [X] Documentation: Pytorch Lightning
- [X] Datacamp: Introduction to Deep Learning with PyTorch
- [X] Deeplizard: Neural Network Programming - Deep Learning with PyTorch
- [ ] Udacity: Intro to Deep Learning with PyTorch
- [X] Youtube: PyTorch Lightning 101
- [X] Youtube: SimCLR with PyTorch Lightning
- [ ] Youtube: PyTorch Performance Tuning Guide
26:41:00

- [X] Youtube: Skin Cancer Detection with PyTorch
- [X] [PART 1] Skin Cancer Detection with PyTorch
0:10:21

- [X] [PART 2] Skin Cancer Detection with PyTorch
0:21:57

- [X] [PART 3] Skin Cancer Detection with PyTorch
0:22:24

- [X] [PART 1] Skin Cancer Detection with PyTorch
- [X] Youtube: Learn with Lightning
- [X] Youtube: PyTorch Tutorial - RNN & LSTM & GRU - Recurrent Neural Nets
00:15:51

- [X] Youtube: Pytorch Zero to All
- [X] PyTorch Lecture 01: Overview
0:10:18

- [X] PyTorch Lecture 02: Linear Model
0:12:52

- [X] PyTorch Lecture 03: Gradient Descent
0:08:24

- [X] PyTorch Lecture 04: Back-propagation and Autograd
0:15:25

- [X] PyTorch Lecture 05: Linear Regression in the PyTorch way
0:11:50

- [X] PyTorch Lecture 06: Logistic Regression
0:10:41

- [X] PyTorch Lecture 07: Wide and Deep
0:10:37

- [X] PyTorch Lecture 08: PyTorch DataLoader
0:06:41

- [X] PyTorch Lecture 09: Softmax Classifier
0:18:47

- [X] PyTorch Lecture 10: Basic CNN
0:15:52

- [X] PyTorch Lecture 11: Advanced CNN
0:12:58

- [X] PyTorch Lecture 12: RNN1 - Basics
0:28:47

- [X] PyTorch Lecture 13: RNN 2 - Classification
0:17:22

- [X] PyTorch Lecture 01: Overview
- [X] PyTorch Developer Day 2020 | Full Livestream
- [X] Youtube: Lightning Chat: How a Grandmaster Won a Kaggle Competition Using Pytorch Lightning

- [X] AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
- [X] AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker
- [X] AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications
- [X] AWS: Hands-on Rekognition: Automated Video Editing
- [X] AWS: Introduction to Amazon Comprehend
- [X] AWS: Introduction to Amazon Comprehend Medical
- [X] AWS: Introduction to Amazon Elastic Inference
- [X] AWS: Introduction to Amazon Forecast
- [X] AWS: Introduction to Amazon Lex
- [X] AWS: Introduction to Amazon Personalize
- [X] AWS: Introduction to Amazon Polly
- [X] AWS: Introduction to Amazon SageMaker Ground Truth
- [X] AWS: Introduction to Amazon SageMaker Neo
- [X] AWS: Introduction to Amazon Transcribe
- [X] AWS: Introduction to Amazon Translate
- [X] AWS: Introduction to AWS Marketplace - Machine Learning Category
- [X] AWS: Machine Learning Exam Basics
- [X] AWS: Neural Machine Translation with Sockeye
- [X] AWS: Process Model: CRISP-DM on the AWS Stack
- [X] AWS: Satellite Image Classification in SageMaker
- [X] Datacamp: Introduction to AWS Boto in Python
- [X] edX: Amazon SageMaker: Simplifying Machine Learning Application Development

- [X] Article: From Research to Production with Deep Semi-Supervised Learning
- [X] Article: RecSys 2020 - Takeaways and Notable Papers
- [X] Article: Paper Summary: DeViSE: A Deep Visual-Semantic Embedding Model
- [X] Article: An overview of proxy-label approaches for semi-supervised learning
- [X] Article: Create, Visualize and Interpret Customer Segments
- [ ] Article: A gentle introduction to HDBSCAN and density-based clustering
- [ ] Article: Grouping data points with k-means clustering
- [ ] Article: Soft clustering with Gaussian mixed models (EM)
- [ ] Article: Introduction to autoencoders
- [ ] Article: Variational autoencoders
- [ ] Article: Principal components analysis (PCA)
- [ ] Article: Deep Inside Autoencoders
- [ ] Article: Build a simple Image Retrieval System with an Autoencoder
- [ ] Article: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
- [ ] Article: A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
- [ ] Article: Self-supervised learning: The dark matter of intelligence
- [ ] Article: Contrastive Self-Supervised Learning
- [ ] Article: Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)
- [ ] Article: Affinity Propagation Algorithm Explained
- [ ] Article: Algorithm Breakdown: Affinity Propagation
- [ ] Article: From Autoencoder to Beta-VAE
- [ ] Article: Self-Supervised Representation Learning
- [ ] Article: GANs in computer vision - Introduction to generative learning
- [ ] Article: GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation
- [ ] Article: GANs in computer vision - semantic image synthesis and learning a generative model from a single image
- [ ] Article: GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes
- [ ] Article: GANs in computer vision - Conditional image synthesis and 3D object generation
- [ ] Article: Decrypt Generative Adversarial Networks (GAN)
- [ ] Article: How to Generate Images using Autoencoders
- [ ] Article: Deepfakes: Face synthesis with GANs and Autoencoders
- [ ] Article: EfficientDet Meets Pytorch-Lightning
- [ ] Berkeley: Deep Unsupervised Learning Spring 2020
- [X] L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020
1:10:02

- [X] L2 Autoregressive Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020
2:27:23

- [ ] L3 Flow Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley -- Spring 2020
1:56:53

- [ ] L4 Latent Variable Models (VAE) -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley
2:19:33

- [ ] Lecture 5 Implicit Models -- GANs Part I --- UC Berkeley, Spring 2020
2:32:32

- [ ] Lecture 6 Implicit Models / GANs part II --- CS294-158-SP20 Deep Unsupervised Learning -- Berkeley
2:09:14

- [X] Lecture 7 Self-Supervised Learning -- UC Berkeley Spring 2020 - CS294-158 Deep Unsupervised Learning
2:20:41

- [ ] L8 Round-up of Strengths and Weaknesses of Unsupervised Learning Methods -- UC Berkeley SP20
0:41:51

- [X] L9 Semi-Supervised Learning and Unsupervised Distribution Alignment -- CS294-158-SP20 UC Berkeley
2:16:00

- [ ] L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning
3:09:49

- [X] L11 Language Models -- guest instructor: Alec Radford (OpenAI) --- Deep Unsupervised Learning SP20
2:38:19

- [ ] L12 Representation Learning for Reinforcement Learning --- CS294-158 UC Berkeley Spring 2020
2:01:56

- [X] L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020
- [ ] Datacamp: Customer Segmentation in Python
- [X] Datacamp: Unsupervised Learning in Python
- [X] Deck: Demystifying Self-Supervised Learning for Visual Recognition
- [X] DeepMind: Inefficient Data Efficiency
- [ ] Google: Clustering
- [X] Udacity: Segmentation and Clustering
- [X] Wandb: Unsupervised Visual Representation Learning with SwAV
- [X] Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)
- [X] Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)
- [X] Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)
- [X] Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning
- [X] Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81
- [X] Youtube: Contrastive Clustering with SwAV
- [ ] Youtube: Variational Autoencoders - EXPLAINED!
0:17:36

- [X] Youtube: OptaProAnalyticsForum– Learning to watch football: Self-supervised representations for tracking data
- [X] Youtube: Can a Neural Net tell if an image is mirrored? – Visual Chirality
- [X] Youtube: Deep InfoMax: Learning deep representations by mutual information estimation and maximization
- [ ] Deep Learning Lecture Summer 2020
- [ ] Deep Learning: Unsupervised Learning - Part 1
- [ ] Deep Learning: Unsupervised Learning - Part 2
- [ ] Deep Learning: Unsupervised Learning - Part 3
- [ ] Deep Learning: Unsupervised Learning - Part 4
- [ ] Deep Learning: Unsupervised Learning - Part 5
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 1
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 2
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 3
- [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 4

- [ ] ECCV 2020: New Frontiers for Learning with Limited Labels or Data
- [X] Introduction to New Frontiers on Learning with Limited Labels or Data
- [X] Self-Supervised Part and Viewpoint Discovery from Image Collections
- [X] Learning Visual Correspondences across Instances and Video Frames
- [X] Limitless Labels in a Labelless World: Weak Supervision with Noisy Labels
- [ ] Inverting Neural Networks for Data-free Knowledge Transfer
- [ ] Learning Efficiently with Biologically Inspired Feedback

- [ ] Youtube: Self-Supervised Learning - What is Next? - Workshop at ECCV 2020, August 28th
- [X] Next Challenges for Self-Supervised Learning - Aäron van den Oord
0:20:13

- [X] Perspectives on Unsupervised Representation Learning - Paolo Favaro
0:42:41

- [X] Learning and Transferring Visual Representations with Few Labels - Carl Doersch
0:32:53

- [ ] Multi-view Invariance and Grouping for Self-Supervised Learning - Ishan Misra
0:36:31

- [ ] Representation Learning beyond Instance Discrimination and Semantic Categorization - Stella Yu
0:43:09

- [X] Self-Supervision as a Path to a Post-Dataset Era - Alexei Alyosha Efros
0:38:06

- [ ] Self-Supervision & Modularity: Cornerstones for Generalization in Embodied Agents - Deepak Pathak
0:41:56

- [X] Next Challenges for Self-Supervised Learning - Aäron van den Oord
- [X] Youtube: Marco Cuturi - A Primer on Optimal Transport
- [X] Youtube: Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift
- [X] Youtube: Clustering Algorithms
- [ ] Youtube: Beyond supervised learning

- [X] Article: Understanding ARPA and Language Models
- [X] Article: Transformer-based Encoder-Decoder Models
- [X] Article: Zero-Shot Learning in Modern NLP
- [X] Article: Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
- [X] Article: Text Data Cleanup - Dynamic Embedding Visualisation
- [X] Article: The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
- [X] Article: Intuition & Use-Cases of Embeddings in NLP & beyond
- [X] Article: The Illustrated GPT-2 (Visualizing Transformer Language Models)
- [X] Article: The Illustrated Word2vec
- [X] Article: All Our N-gram are Belong to You
- [X] Article: Introducing spaCy
- [X] Article: DialogRPT with Huggingface Transformers
- [X] Article: How to Outperform GPT-3 by Combining Task Descriptions With Supervised Learning
- [X] Article: How we used Universal Sentence Encoder and FAISS to make our search 10x smarter
- [X] Article: Porting fairseq wmt19 translation system to transformers
- [X] Article: NLP: Pre-trained Sentiment Analysis
- [X] Article: How to build a State-of-the-Art Conversational AI with Transfer Learning
- [X] Article: The Current Best of Universal Word Embeddings and Sentence Embeddings
- [X] Article: Long Short-Term Memory: From Zero to Hero with PyTorch
- [X] Article: Ten trends in Deep learning NLP
- [X] Article: Google mT5 multilingual text-to-text transformer: A Brief Paper Analysis
- [X] Article: String Matching with BERT, TF-IDF, and more!
- [X] Article: Keyword Extraction with BERT
- [X] Article: Creating a class-based TF-IDF with Scikit-Learn
- [X] Article: Topic Modeling with BERT
- [X] Article: NLP Year In Review
- [X] Article: Using an NLP Q&A System To Study Climate Hazards and Nature-Based Solutions
- [X] Article: Building a sentence embedding index with fastText and BM25
- [X] Article: ML and NLP Research Highlights of 2020
- [X] Article: Hugging Face Reads - 01/2021 - Sparsity and Pruning
- [X] Article: Recent Advances in Language Model Fine-tuning
- [X] Article: Understanding building blocks of ULMFIT
- [X] Article: Swiss army knife for unsupervised task solving
- [X] Article: Multi-Label Text Classification
- [X] Article: Key topics extraction and contextual sentiment of users reviews
- [X] Article: 10 Things You Need to Know About BERT and the Transformer Architecture That Are Reshaping the AI Landscape
- [ ] Article: How many data points is a prompt worth?
- [ ] Article: Fuzzy Matching/Fuzzy Logic Explained
- [ ] Article: Understanding BigBird's Block Sparse Attention"
- [ ] Article: Understanding Convolutional Neural Networks for NLP
- [ ] Article: Attention and Memory in Deep Learning and NLP
- [ ] Article: Unsupervised synonym harvesting
- [ ] Article: Hugging Face Reads, Feb. 2021 - Long-range Transformers
- [ ] Article: Maximizing BERT model performance
- [ ] Article: Unsupervised creation of interpretable sentence representations
- [ ] Article: Document search with fragment embeddings
- [ ] Article: Unsupervised NER using BERT
- [ ] Article: Examining BERT’s raw embeddings
- [ ] Article: T5 — a model that explores the limits of transfer learning
- [ ] Article: T5 — XLNet — a clever language modeling solution
- [ ] Article: A review of BERT based models
- [ ] Article: Deconstructing BERT
- [ ] Article: Text classification from few training examples
- [ ] Article: Improved Few-Shot Text classification
- [ ] Article: Solving NER with BERT for any entity type with very little training data (compared to past approaches)
- [ ] Article: Trends in input representation for state-of-art NLP models (2019)
- [ ] Article: Brief review of word embedding families
- [ ] Article: GPT-2 A nascent transfer learning method that could eliminate supervised learning in some NLP tasks
- [ ] Article: Semantic search using BERT embeddings
- [ ] Article: A Survey of Long-Term Context in Transformers
- [ ] Article: A Deep Dive into the Reformer
- [ ] Article: Representation Learning and Retrieval
- [ ] Article: Large Memory Layers with Product Keys
- [ ] Article: Pattern-Exploiting Training
- [ ] Article: Optimal Transport and the Sinkhorn Transformer
- [ ] Article: Talking-Heads Attention
- [ ] Article: Rebuilding the most popular spellchecker. Part 1
- [ ] Article: Rebuilding the spellchecker, pt.2: Just look in the dictionary, they said!
- [ ] Article: Rebuilding the spellchecker, pt.3: Lookup—compounds and solutions
- [ ] Article: Rebuilding the spellchecker, pt.4: Introduction to suggest algorithm
- [ ] Article: Rebuilding the spellchecker: Hunspell and the order of edits
- [ ] Article: Performers: The Kernel Trick, Random Fourier Features, and Attention
- [ ] Article: Advance BERT model via transferring knowledge from Cross-Encoders to Bi-Encoders
- [ ] Article: Interactive Topic Modeling with BERTopic
- [ ] Article: Commonsense Reasoning for Natural Language Processing
- [ ] Article: Language Models
- [ ] Article: Zero shot NER using RoBERTA
- [ ] Article: Understanding Climate Change Domains through Topic Modeling
- [ ] Article: Simple PyTorch Transformer Example with Greedy Decoding
- [ ] Article: Topic Modeling for Keyword Extraction
- [ ] Article: Poor man’s GPT-3: Few shot text generation with T5 Transformer
- [ ] Article: Paraphrasing
- [ ] Article: Spelling Correction: How to make an accurate and fast corrector
- [ ] Article: Text Generation
- [ ] Article: How to steal modern NLP systems with gibberish?
- [ ] Article: Part of Speech Tagging with Hidden Markov Chain Models
- [ ] Article: Building RNNs is Fun with PyTorch and Google Colab
- [ ] Article: Automatically Summarize Trump’s State of the Union Address
- [ ] Article: Evaluation Metrics for Language Modeling
- [ ] Article: Does GPT-2 Know Your Phone Number?
- [ ] Article: ColumnTransformer Meets Natural Language Processing
- [ ] Article: pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know
- [ ] Article: Explain NLP models with LIME & SHAP
- [ ] Article: When Topic Modeling is Part of the Text Pre-processing
- [ ] Article: Automatic Topic Labeling in 2018: History and Trends
- [ ] Article: The Annotated Transformer
- [ ] Article: Question Classification using Self-Attention Transformer — Part 1
- [ ] Article: Question Classification using Self-Attention Transformer — Part 1.1
- [ ] Article: Question Classification using Self-Attention Transformer — Part 2
- [ ] Article: Question Classification using Self-Attention Transformer — Part 3
- [X] Article: Attention? An Other Perspective!: Part 1
- [ ] Article: Attention? An Other Perspective!: Part 2
- [ ] Article: Attention? An Other Perspective!: Part 3
- [ ] Article: Attention? An Other Perspective!: Part 4
- [ ] Article: Attention? An Other Perspective!: Part 5
- [ ] Article: On word embeddings - Part 1
- [ ] Article: On word embeddings - Part 2: Approximating the Softmax
- [ ] Article: On word embeddings - Part 3: The secret ingredients of word2vec
- [ ] Article: A survey of cross-lingual word embedding models
- [ ] Article: Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models
- [ ] Article: Semantic search using BERT embeddings
- [ ] Article: GPT-2 A nascent transfer learning method that could eliminate supervised learning in some NLP tasks
- [ ] Article: Brief review of word embedding families (2019)
- [ ] Article: Trends in input representation for state-of-art NLP models (2019)
- [ ] Article: An Overview of Multi-Task Learning in Deep Neural Networks
- [ ] Article: Deep Learning for NLP Best Practices
- [ ] Article: Learning to select data for transfer learning
- [ ] Article: Multi-Task Learning Objectives for Natural Language Processing
- [ ] Article: Word embeddings in 2017: Trends and future direction
- [ ] Article: Tracking the Progress in Natural Language Processing
- [ ] Article: NLP's ImageNet moment has arrived
- [ ] Article: A Review of the Neural History of Natural Language Processing
- [ ] Article: 10 Exciting Ideas of 2018 in NLP
- [ ] Article: The 4 Biggest Open Problems in NLP
- [ ] Article: Neural Transfer Learning for Natural Language Processing
- [ ] Article: The State of Transfer Learning in NLP
- [ ] Article: Unsupervised Cross-lingual Representation Learning
- [ ] Article: Why You Should Do NLP Beyond English
- [ ] Article: A review of BERT based models
- [ ] Article: Character level language model RNN
- [ ] Article: How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning
- [ ] Article: How To Create Data Products That Are Magical Using Sequence-to-Sequence Models
- [ ] Article: State-of-the-Art Language Models in 2020
- [X] Article: UNDERSTANDING WORD2VEC THROUGH CULTURAL DIMENSIONS
- [X] Article: How to solve 90% of NLP problems: a step-by-step guide
- [ ] Article: The Unreasonable Effectiveness of Recurrent Neural Networks
- [ ] Article: How to Apply BERT to Arabic and Other Languages
- [X] Article: The Illustrated Transformer
- [ ] Article: Under the Hood of RNNs
- [ ] Article: Gaussian Mixture Models for Clustering
- [ ] Article: What Semantic Search Can do for You
- [ ] Article: The Annotated GPT-2
- [ ] Article: How to Use n-gram Models to Detect Format Errors in Datasets
- [ ] Article: How to Use n-gram Models to Detect Format Errors in Datasets
- [ ] Article: Shrinking fastText embeddings so that it fits Google Colab
- [ ] Article: Perplexity Intuition (and its derivation)
- [ ] Article: How To Do Things With Words. And Counters
- [X] Article: ML and NLP Publications in 2020
- [X] Article: How GPT3 Works
- [X] Article: Explaining RNNs without neural networks
- [X] Article: Implementing Bengio’s Neural Probabilistic Language Model (NPLM) using Pytorch
- [ ] Doc: Huggingface Summary of the models
- [ ] Doc: Summary of the tokenizers
- [ ] Article: OpenAI's GPT-3 Language Model: A Technical Overview
- [X] Article: Adapting Text Augmentation to Industry problems
- [X] Article: How I Used Deep Learning To Train A Chatbot To Talk Like Me (Sorta)
- [X] Article: Semantic Entailment
- [X] Article: Feature-based Approach with BERT
- [ ] Article: Introduction to recurrent neural networks
- [ ] Article: Aspect-Based Opinion Mining (NLP with Python)
- [X] Article: The Transformer Explained
- [X] Article: Controlling Text Generation with Plug and Play Language Models
- [X] Article: What makes a good conversation?
- [X] Article: NLP for Supervised Learning - A Brief Survey
- [ ] Article: Generating Questions Using Transformers
- [ ] Article: Neural Language Models as Domain-Specific Knowledge Bases
- [ ] Article: Understanding BERT’s Semantic Interpretations
- [ ] Article: Using NLP (BERT) to improve OCR accuracy
- [ ] Article: Hyperparameter Optimization for 🤗Transformers: A guide
- [ ] Article: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime
- [ ] Article: Text Similarities : Estimate the degree of similarity between two texts
- [ ] Article: Reducing Toxicity in Language Models
- [ ] Article: Learning Word Embedding
- [ ] Article: The Transformer Family
- [ ] Article: Generalized Language Models
- [ ] Article: Document clustering
- [ ] Article: The Unreasonable Effectiveness of Recurrent Neural Networks
- [ ] Article: LSTM Primer With Real Life Application( DeepMind Kidney Injury Prediction )*
- [ ] Article: Making sense of LSTMs by example
- [ ] Article: 3 subword algorithms help to improve your NLP model performance
- [ ] Article: Exploring LSTMs
- [ ] Article: Understanding LSTM Networks
- [ ] Article: 74 Summaries of Machine Learning and NLP Research
- [X] Article: Smart Batching Tutorial - Speed Up BERT Training
- [X] Article: GPU Benchmarks for Fine-Tuning BERT
- [X] Article: Existing Tools for Named Entity Recognition
- [X] Article: Domain-Specific BERT Models
- [ ] Article: Search (Pt 1) — A Gentle Introduction
- [ ] Article: Search (Pt 2) — A Semantic Horse Race
- [ ] Article: Search (Pt 3) — Elastic Transformers
- [ ] Article: How to Implement a Beam Search Decoder for Natural Language Processing
- [X] Article: Speller100: Zero-shot spelling correction at scale for 100-plus languages
- [ ] Article: Breaking the spell of the spelling check
- [ ] Article: DaCy: New Fast and Efficient State-of-the-Art in Danish NLP!
- [X] A friendly introduction to Recurrent Neural Networks
- [ ] Book: Embeddings in Natural Language Processing
- [ ] Book: Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax
- [ ] Coursera: Sequence Models
- [ ] Coursera: Natural Language Processing in TensorFlow
- [X] CMU: Low-resource NLP Bootcamp 2020
- [X] CMU Low resource NLP Bootcamp 2020 (1): NLP Tasks
1:46:06

- [X] CMU Low resource NLP Bootcamp 2020 (2): Linguistics - Phonology and Morphology
1:24:08

- [X] CMU Low resource NLP Bootcamp 2020 (3): Machine Translation
1:55:59

- [X] CMU Low resource NLP Bootcamp 2020 (4): Linguistics - Syntax and Morphosyntax
2:00:21

- [X] CMU Low resource NLP Bootcamp 2020 (5): Neural Representation Learning
1:19:57

- [X] CMU Low resource NLP Bootcamp 2020 (6): Multilingual NLP
2:04:34

- [X] CMU Low resource NLP Bootcamp 2020 (7): Speech Synthesis
2:22:14

- [X] CMU Low resource NLP Bootcamp 2020 (1): NLP Tasks
- [ ] CMU: Neural Nets for NLP 2020
- [X] CMU Neural Nets for NLP 2020 (1): Introduction
1:11:38

- [ ] CMU Neural Nets for NLP 2020 (2): Language Modeling, Efficiency/Training Tricks
1:18:31

- [ ] CMU Neural Nets for NLP 2020 (3): Convolutional Neural Networks for Text
0:54:45

- [ ] CMU Neural Nets for NLP 2020 (4): Recurrent Neural Networks
1:11:28

- [X] CMU Neural Nets for NLP 2020 (5): Efficiency Tricks for Neural Nets
1:05:37

- [ ] CMU Neural Nets for NLP 2020 (6): Conditioned Generation
1:07:13

- [ ] CMU Neural Nets for NLP 2020 (7): Attention
1:05:26

- [ ] CMU Neural Nets for NLP 2020 (8): Distributional Semantics and Word Vectors
1:10:45

- [ ] CMU Neural Nets for NLP 2020 (9): Sentence and Contextual Word Representations
1:16:19

- [ ] CMU Neural Nets for NLP 2020 (10): Debugging Neural Nets (for NLP)
1:15:26

- [ ] CMU Neural Nets for NLP 2020 (11): Structured Prediction with Local Independence Assumptions
1:08:38

- [ ] CMU Neural Nets for NLP 2020 (12): Generating Trees Incrementally
1:14:13

- [ ] CMU Neural Nets for NLP 2020 (13): Generating Trees Incrementally
0:51:58

- [ ] CMU Neural Nets for NLP 2020 (14): Search-based Structured Prediction
1:06:19

- [ ] CMU Neural Nets for NLP 2020 (15): Minimum Risk Training and Reinforcement Learning
1:09:16

- [ ] CMU Neural Nets for NLP 2020 (16): Advanced Search Algorithms
1:03:02

- [ ] CMU Neural Nets for NLP 2020 (17): Adversarial Methods
1:14:55

- [ ] CMU Neural Nets for NLP 2020 (18): Models w/ Latent Random Variables
1:13:16

- [ ] CMU Neural Nets for NLP 2020 (19): Unsupervised and Semi-supervised Learning of Structure
1:12:47

- [ ] CMU Neural Nets for NLP 2020 (20): Multitask and Multilingual Learning
1:02:46

- [X] CMU Neural Nets for NLP 2020 (21): Document Level Models
0:52:04

- [ ] CMU Neural Nets for NLP 2020 (22): Neural Nets + Knowledge Bases
1:18:39

- [ ] CMU Neural Nets for NLP 2020 (23): Machine Reading w/ Neural Nets
1:06:11

- [ ] CMU Neural Nets for NLP 2020 (24): Natural Language Generation
1:21:48

- [X] CMU Neural Nets for NLP 2020 (25): Model Interpretation
1:04:11

- [X] CMU Neural Nets for NLP 2020 (1): Introduction
- [X] CMU Multilingual NLP 2020
- [X] CMU Multilingual NLP 2020 (1): Introduction
1:17:29

- [X] CMU Multilingual NLP 2020 (2): Typology - The Space of Language
0:37:13

- [X] CMU Multilingual NLP 2020 (3): Words, Parts of Speech, Morphology
0:38:58

- [X] CMU Multilingual NLP 2020 (4): Text Classification and Sequence Labeling
0:45:56

- [X] CMU Multilingual NLP 2020 (5): Advanced Text Classification/Labeling
0:49:40

- [X] CMU Multilingual NLP 2020 (6): Translation, Evaluation, and Datasets
0:46:17

- [X] CMU Multilingual NLP 2020 (7): Machine Translation/Sequence-to-sequence Models
0:43:51

- [X] CMU Multilingual NLP 2020 (8): Data Augmentation for Machine Translation
0:24:42

- [X] CMU Multilingual NLP 2020 (9): Language Contact and Similarity Across Languages
0:30:25

- [X] CMU Multilingual NLP 2020 (10): Multilingual Training and Cross-lingual Transfer
0:39:58

- [X] CMU Multilingual NLP 2020 (11): Unsupervised Translation
0:51:17

- [X] CMU Multilingual NLP 2020 (12): Code Switching, Pidgins, and Creoles
0:46:37

- [X] CMU Multilingual NLP 2020 (13): Speech
0:41:16

- [X] CMU Multilingual NLP 2020 (14): Automatic Speech Recognition
0:39:33

- [X] CMU Multilingual NLP 2020 (15): Low Resource ASR
0:43:38

- [X] CMU Multilingual NLP 2020 (16): Text to Speech
0:39:00

- [X] CMU Multilingual NLP 2020 (17): Morphological Analysis and Inflection
0:45:22

- [X] CMU Multilingual NLP 2020 (18): Dependency Parsing
0:38:15

- [X] CMU Multilingual NLP 2020 (19): Data Annotation
0:53:08

- [X] CMU Multilingual NLP 2020 (20): Active Learning
0:28:37

- [X] CMU Multilingual NLP 2020 (21): Information Extraction
0:41:00

- [X] CMU Multilingual NLP 2020 (22): Multilingual NLP for Indigenous Languages
1:21:58

- [X] CMU Multilingual NLP 2020 (23): Universal Translation at Scale
1:27:33

- [X] CMU Multilingual NLP 2020 (1): Introduction
- [ ] CMU: MultiModal Machine Learning Fall 2020
- [ ] Lecture 1.1: Course Introduction
- [ ] Lecture 1.2: Multimodal applications and datasets
- [ ] Lecture 2.1: Basic concepts: neural networks
- [ ] Lecture 2.2: Basic concepts: network optimization
- [ ] Lecture 3.1: Visual unimodal representations
- [ ] Lecture 3.2: Language unimodal representations
- [ ] Lecture 4.1: Multimodal representation learning
- [ ] Lecture 4.2: Coordinated representations
- [ ] Lecture 5.1: Multimodal alignment
- [ ] Lecture 5.2: Alignment and representation
- [ ] Lecture 7.1: Alignment and translation
- [ ] Lecture 7.2: Probabilistic graphical models
- [ ] Lecture 8.1: Discriminative graphical models
- [ ] Lecture 8.2: Deep Generative Models
- [ ] Lecture 9.1: Reinforcement learning
- [ ] Lecture 9.2: Multimodal RL
- [ ] Lecture 10.1: Fusion and co-learning
- [ ] Lecture 10.2: New research directions

- [X] Datacamp: Advanced NLP with spaCy
- [X] Datacamp: Building Chatbots in Python
- [X] Datacamp: Clustering Methods with SciPy
- [X] Datacamp: Feature Engineering for NLP in Python
- [X] Datacamp: Machine Translation in Python
- [X] Datacamp: Natural Language Processing Fundamentals in Python
- [X] Datacamp: Natural Language Generation in Python
- [X] Datacamp: RNN for Language Modeling
- [X] Datacamp: Regular Expressions in Python
- [X] Datacamp: Sentiment Analysis in Python
- [ ] Datacamp: Spoken Language Processing in Python
- [ ] Notebook: NNLM - Predict Next Word
- [ ] Notebook: Word2Vec
- [ ] Notebook: FastText Sentence Classification
- [ ] Notebook: TextCNN - Binary Sentiment Classification
- [ ] Notebook: TextRNN - Predict Next Step
- [ ] Notebook: TextLSTM - Autocomplete
- [ ] Notebook: Bi-LSTM - Predict Next Word in Long Sentence
- [ ] Notebook: SeqSeq - Change Word
- [ ] Notebook: Seq2Seq with Attention - Translate
- [ ] Notebook: Bi-LSTM with Attention - Binary Sentiment Classification
- [ ] Notebook: The Transformer - Translate
- [ ] Notebook: The Transformer - Greedy Decoder
- [ ] Notebook: BERT - NSP and MLM
- [ ] RNN and LSTM
- [X] Spacy Tutorial
- [X] Stanford CS224U: Natural Language Understanding | Spring 2019
- [X] Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019
1:12:59

- [X] Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:17:10

- [X] Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:16:52

- [X] Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019
0:38:20

- [X] Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:10:44

- [X] Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:03:23

- [X] Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019
1:19:04

- [X] Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:15:02

- [X] Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:15:35

- [X] Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019
1:23:15

- [X] Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019
1:07:05

- [X] Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019
1:18:32

- [X] Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019
1:11:32

- [X] Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019
1:14:33

- [X] Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019
1:15:11

- [X] Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019
- [X] Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019
- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
1:21:52

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses
1:20:43

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks
1:18:50

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation
1:22:15

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing
1:20:22

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs
1:08:25

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs
1:13:23

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention
1:16:56

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects
1:22:39

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering
1:21:01

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP
1:20:18

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models
1:15:30

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings
1:20:18

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention
0:53:48

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation
1:19:37

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution
1:19:20

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning
1:11:54

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs
1:20:37

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI
0:56:03

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning
1:19:15

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2020 | Low Resource Machine Translation
1:15:45

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2020 | BERT and Other Pre-trained Language Models
0:54:28

- [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
- [ ] Stanford: Natural Language Processing | Dan Jurafsky, Christopher Manning
- [X] Course Introduction
0:12:52

- [X] Regular Expressions
0:11:25

- [X] Regular Expressions in Practical NLP
0:06:05

- [X] Word Tokenization
0:14:26

- [X] Word Normalization and Stemming
0:11:48

- [X] Sentence Segmentation
0:05:34

- [X] Defining Minimum Edit Distance
0:07:05

- [X] Computing Minimum Edit Distance
0:05:55

- [X] Backtrace for Computing Alignments
0:05:56

- [X] Weighted Minimum Edit Distance
0:02:48

- [X] Minimum Edit Distance in Computational Biology
0:09:30

- [X] Introduction to N grams
0:08:41

- [X] Estimating N gram Probabilities
0:09:38

- [X] Evaluation and Perplexity
0:11:09

- [X] Generalization and Zeros
0:05:15

- [X] Smoothing Add One
0:06:31

- [X] Interpolation
0:10:25

- [X] Good Turing Smoothing
0:15:35

- [X] Kneser Ney Smoothing
0:08:59

- [X] The Spelling Correction Task
0:05:40

- [X] The Noisy Channel Model of Spelling
0:19:31

- [X] Real Word Spelling Correction
0:09:20

- [X] State of the Art Systems
0:07:10

- [ ] What is Text Classification
0:08:12

- [ ] Naive Bayes
0:03:20

- [ ] Formalizing the Naive Bayes Classifier
0:09:29

- [ ] Naive Bayes Learning
0:05:23

- [ ] Naive Bayes Relationship to Language Modeling
0:04:36

- [ ] Multinomial Naive Bayes A Worked Example
0:08:59

- [ ] Precision, Recall, and the F measure
0:16:17

- [ ] Text Classification Evaluation
0:07:17

- [ ] Practical Issues in Text Classification
0:05:57

- [ ] What is Sentiment Analysis
0:07:18

- [ ] Sentiment Analysis A baseline algorithm
0:13:27

- [ ] Sentiment Lexicons
0:08:38

- [ ] Learning Sentiment Lexicons
0:14:46

- [ ] Other Sentiment Tasks
0:11:02

- [ ] Generative vs Discriminative Models
0:07:50

- [ ] Making features from text for discriminative NLP models
0:18:12

- [ ] Feature Based Linear Classifiers
0:13:35

- [ ] Building a Maxent Model The Nuts and Bolts
0:08:05

- [ ] Generative vs Discriminative models
0:12:10

- [ ] Maximizing the Likelihood
0:10:30

- [ ] Introduction to Information Extraction
0:09:19

- [ ] Evaluation of Named Entity Recognition
0:06:35

- [ ] Sequence Models for Named Entity Recognition
0:15:06

- [ ] Maximum Entropy Sequence Models
0:13:02

- [ ] What is Relation Extraction
0:09:47

- [ ] Using Patterns to Extract Relations
0:06:17

- [ ] Supervised Relation Extraction
0:10:51

- [ ] Semi Supervised and Unsupervised Relation Extraction
0:09:53

- [ ] The Maximum Entropy Model Presentation
0:12:14

- [ ] Feature Overlap Feature Interaction
0:12:52

- [ ] Conditional Maxent Models for Classification
0:04:11

- [ ] Smoothing Regularization Priors for Maxent Models
0:29:24

- [ ] An Intro to Parts of Speech and POS Tagging
0:13:19

- [ ] Some Methods and Results on Sequence Models for POS Tagging
0:13:04

- [ ] Syntactic Structure Constituency vs Dependency
0:08:46

- [ ] Empirical Data Driven Approach to Parsing
0:07:11

- [ ] The Exponential Problem in Parsing
0:14:31

- [ ] Instructor Chat
0:09:03

- [ ] CFGs and PCFGs
0:15:30

- [ ] Grammar Transforms
0:12:06

- [ ] CKY Parsing
0:23:26

- [ ] CKY Example
0:21:25

- [ ] Constituency Parser Evaluation
0:09:46

- [ ] Lexicalization of PCFGs
0:07:03

- [ ] Charniak's Model
0:18:24

- [ ] PCFG Independence Assumptions
0:09:44

- [ ] The Return of Unlexicalized PCFGs
0:20:53

- [ ] Latent Variable PCFGs
0:12:08

- [ ] Dependency Parsing Introduction
0:10:25

- [ ] Greedy Transition Based Parsing
0:31:05

- [ ] Dependencies Encode Relational Structure
0:07:21

- [ ] Introduction to Information Retrieval
0:09:16

- [ ] Term Document Incidence Matrices
0:08:59

- [ ] The Inverted Index
0:10:43

- [ ] Query Processing with the Inverted Index
0:06:44

- [ ] Phrase Queries and Positional Indexes
0:19:46

- [ ] Introducing Ranked Retrieval
0:04:27

- [ ] Scoring with the Jaccard Coefficient
0:05:07

- [ ] Term Frequency Weighting
0:06:00

- [ ] Inverse Document Frequency Weighting
0:10:17

- [ ] TF IDF Weighting
0:03:42

- [ ] The Vector Space Model
0:16:23

- [ ] Calculating TF IDF Cosine Scores
0:12:48

- [ ] Evaluating Search Engines
0:09:03

- [ ] Word Senses and Word Relations
0:11:50

- [ ] WordNet and Other Online Thesauri
0:06:23

- [ ] Word Similarity and Thesaurus Methods
0:16:18

- [ ] Word Similarity Distributional Similarity I
0:13:15

- [ ] Word Similarity Distributional Similarity II
0:08:16

- [ ] What is Question Answering
0:07:29

- [ ] Answer Types and Query Formulation
0:08:48

- [ ] Passage Retrieval and Answer Extraction
0:06:38

- [ ] Using Knowledge in QA
0:04:25

- [ ] Advanced Answering Complex Questions
0:04:53

- [ ] Introduction to Summarization
0:04:46

- [ ] Generating Snippets
0:07:35

- [ ] Evaluating Summaries ROUGE
0:05:03

- [ ] Summarizing Multiple Documents
0:10:42

- [ ] Instructor Chat II
0:05:24

- [X] Course Introduction
- [X] TextBlob Tutorial Series
- [X] Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams
0:11:01

- [X] NLP Tutorial With TextBlob and Python - Parts of Speech Tagging
0:05:59

- [X] NLP Tutorial With TextBlob & Python - Lemmatizating
0:06:32

- [X] NLP Tutorial with TextBlob & Python - Sentiment Analysis(Polarity,Subjectivity)
0:06:31

- [X] Building a NLP-based Flask App with TextBlob
0:37:30

- [X] Natural Language Processing with Polyglot - Installation & Intro
0:12:49

- [X] Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams
- [ ] Youtube: fast.ai Code-First Intro to Natural Language Processing
- [X] What is NLP? (NLP video 1)
0:22:42

- [X] Topic Modeling with SVD & NMF (NLP video 2)
1:06:39

- [X] Topic Modeling & SVD revisited (NLP video 3)
0:33:05

- [X] Sentiment Classification with Naive Bayes (NLP video 4)
0:58:20

- [ ] Sentiment Classification with Naive Bayes & Logistic Regression, contd. (NLP video 5)
0:51:29

- [ ] Derivation of Naive Bayes & Numerical Stability (NLP video 6)
0:23:56

- [ ] Revisiting Naive Bayes, and Regex (NLP video 7)
0:37:33

- [ ] Intro to Language Modeling (NLP video 8)
0:40:58

- [ ] Transfer learning (NLP video 9)
1:35:16

- [ ] ULMFit for non-English Languages (NLP Video 10)
1:49:22

- [ ] Understanding RNNs (NLP video 11)
0:33:16

- [ ] Seq2Seq Translation (NLP video 12)
0:59:42

- [ ] Word embeddings quantify 100 years of gender & ethnic stereotypes-- Nikhil Garg (NLP video 13)
0:47:17

- [ ] Text generation algorithms (NLP video 14)
0:25:39

- [ ] Implementing a GRU (NLP video 15)
0:23:13

- [ ] Algorithmic Bias (NLP video 16)
1:26:17

- [ ] Introduction to the Transformer (NLP video 17)
0:22:54

- [ ] The Transformer for language translation (NLP video 18)
0:55:17

- [X] What you need to know about Disinformation (NLP video 19)
0:51:21

- [ ] Article: Zero to Hero with fastai - Beginner
- [ ] Article: Zero to Hero with fastai - Intermediate

- [X] What is NLP? (NLP video 1)
- [ ] NLP Course | For You
- [ ] Word Embeddings
- [ ] Text Classification
- [ ] Language Modeling
- [ ] Seq2seq and Attention

- [X] Youtube: BERT Research Series
- [X] YouTube: Intro to NLP with Spacy
- [X] Talk: Practical NLP for the Real World
- [X] YouTube: Level 3 AI Assistant Conference 2020
- [X] Youtube: Conversation Analysis Theory in Chatbots | Michael Szul
- [X] Youtube: Designing Practical NLP Solutions | Ines Montani
- [X] Youtube: Effective Copywriting for Chatbots | Hans Van Dam
- [X] Youtube: Distilling BERT | Sam Sucik
- [X] Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam
- [X] Youtube: From Research to Production – Our Process at Rasa | Tanja Bunk
- [X] Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol

- [X] Youtube: RASA Algorithm Whiteboard
- [X] Introducing The Algorithm Whiteboard
0:01:16

- [X] Rasa Algorithm Whiteboard - Diet Architecture 1: How it Works
0:23:27

- [X] Rasa Algorithm Whiteboard - Diet Architecture 2: Design Decisions
0:15:06

- [X] Rasa Algorithm Whiteboard - Diet Architecture 3: Benchmarking
0:22:34

- [X] Rasa Algorithm Whiteboard - Embeddings 1: Just Letters
0:13:48

- [X] Rasa Algorithm Whiteboard - Embeddings 2: CBOW and Skip Gram
0:19:24

- [X] Rasa Algorithm Whiteboard - Embeddings 3: GloVe
0:19:12

- [X] Rasa Algorithm Whiteboard - Embeddings 4: Whatlies
0:14:03

- [X] Rasa Algorithm Whiteboard - Attention 1: Self Attention
0:14:32

- [X] Rasa Algorithm Whiteboard - Attention 2: Keys, Values, Queries
0:12:26

- [X] Rasa Algorithm Whiteboard - Attention 3: Multi Head Attention
0:10:55

- [X] Rasa Algorithm Whiteboard: Attention 4 - Transformers
0:14:34

- [X] Rasa Algorithm Whiteboard - StarSpace
0:11:46

- [X] Rasa Algorithm Whiteboard - TED Policy
0:16:10

- [X] Rasa Algorithm Whiteboard - TED in Practice
0:14:54

- [X] Rasa Algorithm Whiteboard - Response Selection
0:12:07

- [X] Rasa Algorithm Whiteboard - Response Selection: Implementation
0:09:25

- [X] Rasa Algorithm Whiteboard - Countvectors
0:13:32

- [X] Rasa Algorithm Whiteboard - Subword Embeddings
0:11:58

- [X] Rasa Algorithm Whiteboard - Implementation of Subword Embeddings
0:10:01

- [X] Rasa Algorithm Whiteboard - BytePair Embeddings
0:12:44

- [X] Introducing The Algorithm Whiteboard
- [X] Youtube: A brief history of the Transformer architecture in NLP
- [X] Youtube: The Transformer neural network architecture explained. “Attention is all you need” (NLP)
- [X] Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision
- [X] Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)
- [X] Youtube: Ilya Sutskever - GPT-2
- [X] Youtube: NLP Masterclass | Modeling Fallacies in NLP
- [X] Youtube: What is GPT-3? Showcase, possibilities, and implications
- [X] Youtube: TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP
- [X] Article: How the Embedding Layers in BERT Were Implemented
- [X] Youtube: Easy Data Augmentation for Text Classification
- [X] Youtube: Webinar: Special NLP Session with Hugging Face
- [X] Youtube: Spacy IRL 2019
- [X] Sebastian Ruder: Transfer Learning in Open-Source Natural Language Processing (spaCy IRL 2019)
0:31:24

- [X] Giannis Daras: Improving sparse transformer models for efficient self-attention (spaCy IRL 2019)
0:20:13

- [X] Peter Baumgartner: Applied NLP: Lessons from the Field (spaCy IRL 2019)
0:18:44

- [X] Justina Petraitytė: Lessons learned in helping ship conversational AI assistants (spaCy IRL 2019)
0:23:48

- [X] Yoav Goldberg: The missing elements in NLP (spaCy IRL 2019)
0:30:27

- [X] Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019)
0:20:08

- [X] Guadalupe Romero: Rethinking rule-based lemmatization (spaCy IRL 2019)
0:14:49

- [X] Mark Neumann: ScispaCy: A spaCy pipeline & models for scientific & biomedical text (spaCy IRL 2019)
0:18:59

- [X] Patrick Harrison: Financial NLP at S&P Global (spaCy IRL 2019)
0:21:42

- [X] McKenzie Marshall: NLP in Asset Management (spaCy IRL 2019)
0:20:32

- [X] David Dodson: spaCy in the News: Quartz's NLP pipeline (spaCy IRL 2019)
0:20:56

- [X] Matthew Honnibal & Ines Montani: spaCy and Explosion: past, present & future (spaCy IRL 2019)
0:54:13

- [X] Sebastian Ruder: Transfer Learning in Open-Source Natural Language Processing (spaCy IRL 2019)
- [ ] Youtube: The Future of Natural Language Processing
- [X] Youtube: Sentiment Analysis: Key Milestones, Challenges and New Directions
- [X] Youtube: Simple and Efficient Deep Learning for Natural Language Processing, with Moshe Wasserblat, Intel AI
- [X] Youtube: Why not solve biological problems with a Transformer? BERTology meets Biology
- [X] Youtube: Self-attention step-by-step | How to get meaning from text
- [X] Youtube: Chat Bot with PyTorch
- [ ] Youtube: NLP with Friends Talks
- [X] NLP with Friends, Featured Friend: Tom McCoy
0:36:48

- [X] NLP with Friends, Featured Friend: Maarten Sap
0:36:11

- [ ] NLP with Friends, featured friend: Nitika Mathur
1:01:42

- [ ] NLP with Friends, Featured Friend: Sabrina J Mielke
1:01:28

- [X] NLP with Friends, Featured Friend: Tom McCoy
- [X] Youtube: Insincere Question Classification with PyTorch
- [ ] Crash Course: Linguistics
- [X] Crash Course Linguistics Preview
0:02:50

- [X] What is Linguistics?: Crash Course Linguistics #1
0:11:11

- [X] Crash Course Linguistics Preview
- [X] Youtube: Recent Advances in Language Pretraining and Generation
- [X] Youtube: Talks # 3: Lorenzo Ampil - Introduction to T5 for Sentiment Span Extraction
- [X] Youtube: Frontiers in ML: Learning from Limited Labeled Data: Challenges and Opportunities for NLP
- [X] Youtube: DeepLearning.ai NLP talk: Chris Manning
- [X] Youtube: DeepLearning.ai NLP talk: Oren Etzioni
- [X] Youtube: DeepLearning.ai NLP talk: Quoc Le
- [X] Youtube: What can MIR learn from transfer learning in NLP?
- [X] Youtube: The Narrated Transformer Language Model
- [X] Youtube: spaCy v3.0: Bringing State-of-the-art NLP from Prototype to Production
00:22:40

- [X] Youtube: Conversational AI with Transformers and Rule-Based Systems
1:53:24

- [X] Talk: High Performance Natural Language Processing
- [X] Talk: EmoTag1200: Understanding the Association between Emojis and Emotions
- [ ] Youtube: Research Paper Walkthrough
- [X] Simple Unsupervised Keyphrase Extraction using Sentence Embeddings | Research Paper Walkthrough
0:21:23

- [X] Leveraging BERT for Extractive Text Summarization on Lectures | Research Paper Walkthrough
0:20:10

- [X] Data Augmentation Techniques for Text Classification in NLP | Research Paper Walkthrough
0:14:33

- [X] CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery | Research Paper Walkthrough
0:23:47

- [X] Data Augmentation using Pre-trained Transformer Model (BERT, GPT2, etc) | Research Paper Walkthrough
0:17:43

- [X] A Supervised Approach to Extractive Summarisation of Scientific Papers | Research Paper Walkthrough
0:19:01

- [X] BLEURT: Learning Robust Metrics for Text Generation | Research Paper Walkthrough
0:13:38

- [X] DeepWalk: Online Learning of Social Representations | ML with Graphs | Research Paper Walkthrough
0:17:44

- [X] LSBert: A Simple Framework for Lexical Simplification | Research Paper Walkthrough
0:20:27

- [X] SpanBERT: Improving Pre-training by Representing and Predicting Spans | Research Paper Walkthrough
0:14:21

- [X] Text Summarization of COVID-19 Medical Articles using BERT and GPT-2 | Research Paper Walkthrough
0:21:52

- [X] Extractive & Abstractive Summarization with Transformer Language Models | Research Paper Walkthrough
0:16:58

- [X] Unsupervised Multi-Document Summarization using Neural Document Model | Research Paper Walkthrough
0:15:11

- [X] SummPip: Multi-Document Summarization with Sentence Graph Compression | Research Paper Walkthrough
0:16:54

- [X] Combining BERT with Static Word Embedding for Categorizing Social Media | Research Paper Walkthrough
0:13:51

- [X] Reformulating Unsupervised Style Transfer as Paraphrase Generation | Research Paper Walkthrough
0:19:41

- [X] PEGASUS: Pre-training with Gap-Sentences for Abstractive Summarization | Research Paper Walkthrough
0:15:04

- [X] Evaluation of Text Generation: A Survey | Human-Centric Evaluations | Research Paper Walkthrough
0:15:54

- [X] TOD-BERT: Pre-trained Transformers for Task-Oriented Dialogue Systems (Research Paper Walkthrough)
0:15:25

- [X] TextRank: Bringing Order into Texts (Research Paper Walkthrough)
0:14:34

- [X] Node2Vec: Scalable Feature Learning for Networks | ML with Graphs (Research Paper Walkthrough)
0:14:33

- [X] HARP: Hierarchical Representation Learning for Network | ML with Graphs (Research Paper Walkthrough)
0:15:10

- [X] URL2Video: Automatic Video Creation From a Web Page | AI and Creativity (Research Paper Walkthrough)
0:15:21

- [X] On Generating Extended Summaries of Long Documents (Research Paper Walkthrough)
0:14:24

- [X] Nucleus Sampling: The Curious Case of Neural Text Degeneration (Research Paper Walkthrough)
0:12:48

- [X] T5: Exploring Limits of Transfer Learning with Text-to-Text Transformer (Research Paper Walkthrough)
0:12:47

- [X] DialoGPT: Generative Training for Conversational Response Generation (Research Paper Walkthrough)
0:13:17

- [X] Hierarchical Transformers for Long Document Classification (Research Paper Walkthrough)
0:12:46

- [ ] Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (Best Paper ACL 2020)
0:14:00

- [X] Simple Unsupervised Keyphrase Extraction using Sentence Embeddings | Research Paper Walkthrough
- [X] NLP Summit 2020
- [X] The 2020 Trends for Applied Natural Language Processing | NLP Summit 2020
0:21:10

- [X] NLP Industry Survey Analysis: the landscape of natural language use cases in 2020 | NLP Summit 2020
0:20:23

- [X] Auto NLP: Pretrain, Tune & Deploy State-of-the-art Models Without Coding
0:19:57

- [X] Proof-of-Concept Delight | NLP Summit 2020
0:16:50

- [X] Distributed Natural Language Processing Apps for Financial Engineering | NLP Summit 2020
0:34:49

- [X] Bleeding Edge Applications of 2020 Transformers | NLP Summit 2020
0:33:34

- [X] How Freshworks Freddy AI leverages NLP for Ethics-First Customer Experiences | NLP Summit 2020
0:26:49

- [X] NLP for Recruitment Automation: Building a Chatbot from the Job Description | NLP Summit 2020
0:22:31

- [X] The 2020 Trends for Applied Natural Language Processing | NLP Summit 2020
- [ ] Youtube: Explainability for Natural Language Processing
- [X] Youtube: Gibberish Detector
- [X] Youtube: NLP Lecture 7 Constituency Parsing
- [X] NLP Lecture 7 - Overview of Constituency Parsing Lecture
0:01:50

- [X] NLP Lecture 7 - Introduction to Constituency Parsing
0:10:29

- [X] NLP Lecture 7(a) - Context Free Grammar
0:17:03

- [X] NLP Lecture 7(b) - Constituency Parsing
0:13:28

- [X] NLP Lecture 7(c) - Statistical Constituency Parsing
0:09:38

- [X] NLP Lecture 7(d) - Dependency Parsing
0:17:15

- [X] NLP Lecture 7 - Overview of Constituency Parsing Lecture
- [X] Youtube: LING 83 Teaching Video: Constituency Parsing
- [ ] Youtube: SpaCy for Digital Humanities with Python Tutorials
- [ ] Introduction to SpaCy and Cleaning Data (SpaCy and Python Tutorials for DH - 01)
0:06:07

- [ ] How to Install SpaCy and Models (Spacy and Python Tutorial for DH 02)
0:07:40

- [ ] How to Separate Sentences in SpaCy (SpaCy and Python Tutorials for DH - 03)
0:08:33

- [ ] Spacy and Named Entity Recognition NER (Spacy and Python Tutorial for DH 04)
0:08:32

- [ ] Finding Parts of Speech (SpaCy and Python Tutorial for DH 05)
0:02:55

- [ ] Extracting Nouns and Noun Chunks (SpaCy and Python Tutorial for DH 06)
0:05:46

- [ ] Extracting Verbs and Verb Phrases (SpaCy and Python Tutorial for DH 07)
0:08:10

- [ ] Lemmatization: Finding the Roots of Words (Spacy and Python Tutorial for DH 08)
0:04:52

- [ ] Data Visualization with DisplaCy (Spacy and Python Tutorial for DH 09)
0:09:13

- [ ] Customizing DisplaCy Render Data Visualization (Spacy and Python Tutorial for DH 10)
0:08:19

- [ ] Finding Quotes in Sentences (SpaCy and Python Tutorial for DH 11)
0:08:45

- [ ] Introduction to Named Entity Recognition (NER for DH 01)
0:16:43

- [ ] Machine Learning NER with Python and spaCy (NER for DH 03 )
0:13:36

- [ ] How to Use spaCy's EntityRuler (Named Entity Recognition for DH 04 | Part 01)
0:36:50

- [ ] How to Use spaCy to Create an NER training set (Named Entity Recognition for DH 04 | Part 02)
0:10:32

- [ ] How to Train a spaCy NER model (Named Entity Recognition for DH 04 | Part 03)
0:15:40

- [ ] Examining a spaCy Model in the Folder (Named Entity Recognition for DH 05)
0:15:06

- [ ] What are Word Vectors (Named Entity Recognition for DH 06)
0:18:49

- [ ] How to Generate Custom Word Vectors in Gensim (Named Entity Recognition for DH 07)
0:23:05

- [ ] How to Load Custom Word Vectors into spaCy Models (Named Entity Recognition for DH 08)
0:10:46

- [ ] Getting the Data for Custom Labels (Holocaust NER for DH 09.01)
0:11:00

- [ ] How to Add a Custom NER Pipe in spaCy and a Custom Label (NER for DH 09.02 )
0:07:49

- [ ] How to Training Custom Entities into spaCy Models (Named Entity Recognition for DH 09 03)
0:15:29

- [ ] How to Add and Place Pipes from other Models into a New Model (NER for DH 09 04)
0:12:24

- [ ] How to Add Custom Functions to spaCy Pipeline (NER for DH 09.05)
0:15:20

- [ ] Precision vs. Recall and Adding PERSON to Holocaust NER Pipeline (Named Entity Recognition DH 09.06)
0:26:02

- [ ] Finalizing the Holocaust NER Pipeline (Named Entity Recognition for DH 09.07)
0:14:16

- [ ] Classical Latin Named Entity Recognition (NER for DH 10.01)
0:55:30

- [ ] How to Package spaCy Models (Even with Custom Factories) (NER for DH 11)
0:15:31

- [ ] Introduction to SpaCy and Cleaning Data (SpaCy and Python Tutorials for DH - 01)
- [ ] Youtube: Billion-scale Approximate Nearest Neighbor Search
- [X] Youtube: Data Science - Fuzzy Record Matching
- [X] Youtube: Minimum Edit Distance Dynamic Programming
- [X] Youtube: Cheuk Ting Ho - Fuzzy Matching Smart Way of Finding Similar Names Using Fuzzywuzzy
- [X] Youtube: What's in a Name? Fast Fuzzy String Matching - Seth Verrinder & Kyle Putnam - Midwest.io 2015
- [X] Youtube: Jiaqi Liu Fuzzy Search Algorithms How and When to Use Them PyCon 2017
- [X] Youtube: 1 + 1 = 1 or Record Deduplication with Python | Flávio Juvenal @ PyBay2018
- [X] Youtube: Mike Mull: The Art and Science of Data Matching
- [X] Youtube: Record linkage: Join for real life by Rhydwyn Mcguire
- [X] Youtube: Approximate nearest neighbors and vector models, introduction to Annoy
- [X] Youtube: Librosa Audio and Music Signal Analysis in Python | SciPy 2015 | Brian McFee
- [ ] Video: Recent Advances in LM Pre-training
- [X] Youtube: Deep Learning (for Audio) with Python
- [X] 1- Deep Learning (for Audio) with Python: Course Overview
0:08:02

- [X] 2- AI, machine learning and deep learning
0:31:15

- [X] 3- Implementing an artificial neuron from scratch
0:19:05

- [X] 4- Vector and matrix operations
0:25:51

- [X] 5- Computation in neural networks
0:23:19

- [X] 6- Implementing a neural network from scratch in Python
0:21:03

- [X] 7- Training a neural network: Backward propagation and gradient descent
0:21:41

- [X] 8- TRAINING A NEURAL NETWORK: Implementing backpropagation and gradient descent from scratch
1:03:00

- [X] 9- How to implement a (simple) neural network with TensorFlow 2
0:24:37

- [X] 10 - Understanding audio data for deep learning
0:32:55

- [X] 11- Preprocessing audio data for Deep Learning
0:25:05

- [X] 12- Music genre classification: Preparing the dataset
0:37:45

- [X] 13- Implementing a neural network for music genre classification
0:33:25

- [X] 14- SOLVING OVERFITTING in neural networks
0:26:29

- [X] 15- Convolutional Neural Networks Explained Easily
0:35:23

- [X] 16- How to Implement a CNN for Music Genre Classification
0:49:10

- [X] 17- Recurrent Neural Networks Explained Easily
0:28:35

- [X] 18- Long Short Term Memory (LSTM) Networks Explained Easily
0:28:08

- [X] 19- How to Implement an RNN-LSTM Network for Music Genre Classification
0:14:29

- [X] 1- Deep Learning (for Audio) with Python: Course Overview
- [ ] Youtube: Fine-tuning a large language model without your own supercomputer
- [X] Youtube: How to build a custom spell checker using python NLP

- [ ] Google: Recommendation Systems
- [X] Pluralsight: Understanding Algorithms for Recommendation Systems
- [X] Youtube: Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg
- [X] Youtube: Learning "Learning to Rank"
- [X] Youtube: Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]

- [X] Article: The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
- [X] Article: How to extract Key-Value pairs from Documents using deep learning
- [ ] Article: Essential Pil (Pillow) Image Tutorial (for Machine Learning People)
- [ ] Article: What is Focal Loss and when should you use it?
- [ ] Article: Part 1: Deep Representations, a way towards neural style transfer
- [ ] Article: A gentle introduction to OCR
- [ ] Article: Breaking Linear Classifiers on ImageNet
- [ ] Article: Building an image search service from scratch
- [ ] Article: Squeeze and Excitation Networks Explained with PyTorch Implementation
- [ ] Article: Multimodal Neurons in Artificial Neural Networks
- [ ] Article: DenseNet Architecture Explained with PyTorch Implementation from TorchVision
- [ ] Article: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- [ ] Article: Group Normalization
- [ ] Article: A Short Introduction to Generative Adversarial Networks
- [ ] Article: Semi-supervised Learning with GANs
- [ ] Article: Densely Connected Convolutional Networks in Tensorflow
- [ ] Article: Convolutional neural networks
- [ ] Article: Common architectures in convolutional neural networks
- [ ] Article: An overview of semantic image segmentation
- [ ] Article: Evaluating image segmentation models
- [ ] Article: An overview of object detection: one-stage methods
- [ ] Article: A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
- [ ] Article: Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS
- [ ] Article: Object Detection for Dummies Part 2: CNN, DPM and Overfeat
- [ ] Article: Object Detection for Dummies Part 3: R-CNN Family
- [ ] Article: Understanding coordinate systems and DICOM for deep learning medical image analysis
- [ ] Article: Understanding the receptive field of deep convolutional networks
- [ ] Article: Deep learning in medical imaging - 3D medical image segmentation with PyTorch
- [ ] Article: Intuitive Explanation of Skip Connections in Deep Learning
- [ ] Article: Human Pose Estimation
- [ ] Article: YOLO - You only look once (Single shot detectors)
- [ ] Article: Localization and Object Detection with Deep Learning
- [ ] Article: Semantic Segmentation in the era of Neural Networks
- [ ] Article: ECCV 2020: Some Highlights
- [ ] Article: NonCompositional
- [X] Article: Looking Inside The Blackbox — How To Trick A Neural Network
- [X] AWS: Semantic Segmentation Explained
- [ ] Book: Deep Learning for Computer Vision with Python
- [ ] Book: Practical Python and OpenCV
- [ ] Coursera: Convolutional Neural Networks
- [ ] Datacamp: Biomedical Image Analysis in Python
- [ ] Datacamp: Image Processing in Python
- [X] Google: ML Practicum: Image Classification
- [ ] Stanford: CS231N Winter 2016
- [X] CS231n Winter 2016: Lecture 1: Introduction and Historical Context
1:19:08

- [X] CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1
0:57:28

- [ ] CS231n Winter 2016: Lecture 3: Linear Classification 2, Optimization
1:11:23

- [ ] CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1
1:19:38

- [ ] CS231n Winter 2016: Lecture 5: Neural Networks Part 2
1:18:37

- [ ] CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNets
1:09:35

- [ ] CS231n Winter 2016: Lecture 7: Convolutional Neural Networks
1:19:01

- [ ] CS231n Winter 2016: Lecture 8: Localization and Detection
1:04:57

- [ ] CS231n Winter 2016: Lecture 9: Visualization, Deep Dream, Neural Style, Adversarial Examples
1:18:20

- [ ] CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM
1:09:54

- [ ] CS231n Winter 2016: Lecture 11: ConvNets in practice
1:15:03

- [ ] CS231n Winter 2016: Lecture 12: Deep Learning libraries
1:21:06

- [ ] CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning
1:17:36

- [ ] CS231n Winter 2016: Lecture 13: Segmentation, soft attention, spatial transformers
1:10:59

- [X] CS231n Winter 2016: Lecture 15: Invited Talk by Jeff Dean
1:14:49

- [X] CS231n Winter 2016: Lecture 1: Introduction and Historical Context
- [ ] Udacity: Introduction to Computer Vision
- [X] Youtube: Deep Residual Learning for Image Recognition (Paper Explained)
- [X] Youtube: Implementing ResNet from scratch
- [ ] Youtube: ConvNets Scaled Efficiently
0:13:19

- [ ] Youtube: Building an Image Captioner with Neural Networks
0:12:54

- [ ] Youtube: Evolution of Face Generation | Evolution of GANs
0:12:23

- [ ] Youtube: Autoencoders - EXPLAINED
0:10:53

- [ ] Youtube: Unpaired Image-Image Translation using CycleGANs
0:16:22

- [ ] Youtube: AI creates Image Classifiers…by DRAWING?
0:09:04

- [ ] Youtube: The Evolution of Convolution Neural Networks
0:24:02

- [ ] Youtube: Depthwise Separable Convolution - A FASTER CONVOLUTION!
0:12:43

- [ ] Youtube: Mask Region based Convolution Neural Networks - EXPLAINED!
0:09:34

- [ ] Youtube: Sound play with Convolution Neural Networks
0:11:57

- [ ] Youtube: Convolution Neural Networks - EXPLAINED
0:19:20

- [ ] Youtube: Generative Adversarial Networks - FUTURISTIC & FUN AI !
0:14:20

- [ ] Youtube: How Convolution Works
- [X] Youtube: DETR: End-to-End Object Detection with Transformers (Paper Explained)
- [X] Youtube: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)

- [ ] Datacamp: Machine Learning for Finance in Python
- [X] Datacamp: Introduction to Time Series Analysis in Python
- [ ] Datacamp: Machine Learning for Time Series Data in Python
- [ ] Datacamp: Intro to Portfolio Risk Management in Python
- [ ] Datacamp: Financial Forecasting in Python
- [X] Datacamp: Predicting CTR with Machine Learning in Python
- [X] Datacamp: Intro to Financial Concepts using Python
- [X] Datacamp: Fraud Detection in Python
- [ ] Datacamp: Forecasting Using ARIMA Models in Python
- [ ] Datacamp: Introduction to Portfolio Analysis in Python
- [ ] Datacamp: Credit Risk Modeling in Python
- [ ] Datacamp: Machine Learning for Marketing in Python
- [ ] Udacity: Machine Learning for Trading
- [ ] Udacity: Time Series Forecasting

- [X] DeepLizard: Reinforcement Learning - Goal Oriented Intelligence
- [X] Reinforcement Learning Series Intro - Syllabus Overview
0:05:51

- [X] Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning Problem
0:06:34

- [X] Expected Return - What Drives a Reinforcement Learning Agent in an MDP
0:06:47

- [X] Policies and Value Functions - Good Actions for a Reinforcement Learning Agent
0:06:52

- [X] What do Reinforcement Learning Algorithms Learn - Optimal Policies
0:06:21

- [X] Q-Learning Explained - A Reinforcement Learning Technique
0:08:37

- [X] Exploration vs. Exploitation - Learning the Optimal Reinforcement Learning Policy
0:10:06

- [X] OpenAI Gym and Python for Q-learning - Reinforcement Learning Code Project
0:07:52

- [X] Train Q-learning Agent with Python - Reinforcement Learning Code Project
0:08:59

- [X] Watch Q-learning Agent Play Game with Python - Reinforcement Learning Code Project
0:07:22

- [X] Deep Q-Learning - Combining Neural Networks and Reinforcement Learning
0:10:50

- [X] Replay Memory Explained - Experience for Deep Q-Network Training
0:06:21

- [X] Training a Deep Q-Network - Reinforcement Learning
0:09:07

- [X] Training a Deep Q-Network with Fixed Q-targets - Reinforcement Learning
0:07:35

- [X] Deep Q-Network Code Project Intro - Reinforcement Learning
0:06:26

- [X] Build Deep Q-Network - Reinforcement Learning Code Project
0:16:51

- [X] Deep Q-Network Image Processing and Environment Management - Reinforcement Learning Code Project
0:21:53

- [X] Deep Q-Network Training Code - Reinforcement Learning Code Project
0:19:46

- [X] Reinforcement Learning Series Intro - Syllabus Overview

- [X] A recipe for training neural networks
- [ ] Article: Evaluating a machine learning model
- [ ] Article: Hyperparameter tuning for machine learning models
- [ ] Article: Hacker's Guide to Hyperparameter Tuning
- [ ] Article: Environment and Distribution Shift
- [ ] Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- [X] Datacamp: Model Validation in Python
- [X] Datacamp: Hyperparameter Tuning in Python
- [ ] Google: Testing and Debugging
- [ ] Troubleshooting Deep Neural Networks
- [ ] Youtube: How do GPUs speed up Neural Network training?
0:08:19

- [ ] Youtube: Why use GPU with Neural Networks?
0:09:43

- [X] Youtube: Auto-Tuning Hyperparameters with Optuna and PyTorch

- [X] Article: How to leverage Explainable Machine Learning
- [X] Article: TracIn — A Simple Method to Estimate Training Data Influence
- [ ] NeurIPS 2020: Tutorial on Explaining ML Predictions: State-of-the-art, Challenges, and Opportunities
- [ ] Youtube: Jay Alammar - Take A Look Inside Language Models With Ecco

- [X] Article: A Survey of Methods for Model Compression in NLP
- [X] Article: Why you should convert your NLP pipelines to ONNX
- [ ] Article: Neural Network Pruning
- [ ] Article: FasterAI
- [X] Article: Is the future of Neural Networks Sparse? An Introduction (1/N)
- [X] Article: Sparse Neural Networks (2/N): Understanding GPU Performance.
- [X] Article: Block Sparse Matrices for Smaller and Faster Language Models
- [ ] Article: Plunging Into Model Pruning in Deep Learning
- [ ] Article: How to accelerate and compress neural networks with quantization

- [X] Article: Effective testing for machine learning systems
- [X] Article: Unit Testing for Data Scientists
- [ ] Article: Testing in Production, the safe way
- [X] Article: How to cheat at unit tests with pytest and Black
- [X] Article: 4 Lesser-Known Yet Awesome Tips for Pytest
- [X] Datacamp: Unit Testing for Data Science in Python
- [X] Pluralsight: Test-driven Development: The Big Picture
- [ ] Test Driven Development with Python
- [ ] Thoughtbot: Fundamentals of TDD
- [ ] Udacity: Software Analysis & Testing
- [ ] Udacity: Software Testing
- [ ] Udacity: Software Debugging
- [X] Youtube: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList | AISC

- [X] Article: Deploy a Keras Deep Learning Project to Production with Flask
- [X] Article: Deploy BERT for Sentiment Analysis as REST API using PyTorch, Transformers by Hugging Face and FastAPI
- [ ] Article: Microservice in Python using FastAPI
- [X] Article: Selecting gunicorn worker types for different python web applications.
- [X] Article: Better performance by optimizing Gunicorn config
- [X] Article: Exponential Backoff And Jitter
- [X] Django Best Practices
- [ ] Udacity: Authentication & Authorization: OAuth
- [ ] Udacity: HTTP & Web Servers
- [ ] Udacity: Designing RESTful APIs
- [ ] Udacity: Client-Server Communication
- [X] Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPI
- [X] Youtube: FastAPI from the ground up
- [X] Youtube: Python pydantic Introduction – Give your data classes super powers

- [X] Article: Build and Deploy a Dashboard with Streamlit
- [X] Article: New layout options for Streamlit

- [X] Acloudguru: AWS Certified Machine Learning - Specialty
- [X] Acloudguru: AWS Certified Developer - Associate
- [X] Acloudguru: AWS Certification Preparation Guide
- [X] AWS: Exam Readiness: AWS Certified Developer – Associate
- [X] AWS: Thirty Serverless Architectures in 30 Minutes
- [X] Article: Celery Execution Pools: What is it all about?
- [X] Article: Getting machine learning to production
- [X] Article: A Guide to Production Level Deep Learning
- [X] Article: MLOps concepts for busy engineers: model serving
- [X] Article: How to put machine learning models into production
- [X] Article: Monitoring your Machine Learning Model
- [X] Article: How to Deploy a Machine Learning Model
- [X] Article: Smaller Docker images with Conda
- [ ] Article: Deploying conda environments in (Docker) containers - how to do it right
- [X] Article: How to scale services using Docker Compose
- [X] Article: Understand Linux Load Averages and Monitor Performance of Linux
- [X] Article: Preventing model drift with continuous monitoring and deployment using Github Actions and Algorithmia Insights
- [X] Article: Building a feature store
- [X] Article: Combining rule engines and machine learning
- [X] Article: Model artifacts: the war stories
- [X] Article: Shadow mode deployments
- [X] Article: When is a neural net too big for production?
- [X] Article: Tensorflow in Docker
- [X] Article: How to build scalable Machine Learning systems — Part 1/2
- [X] Article: Architecting a Machine Learning Pipeline
- [X] Article: Deploying Machine Learning Models: A Checklist
- [X] Article: How to Serve Models
- [X] Article: How to Monitor Models
- [X] Article: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance
- [X] Article: Machine learning is going real-time
- [X] Article: The correct way to evaluate online machine learning models
- [X] Article: Machine Learning to Production
- [X] Article: Online batching with Spell serving
- [X] Article: A deep dive into AWS spot instance interruptions
- [X] Article: Key Concepts for Deploying Machine Learning Models to Mobile
- [X] Article: Configuring Gunicorn for Docker
- [X] Article: How To Pass Environment Info During Docker Builds
- [X] Article: Pass Docker Environment Variables During The Image Build
- [X] Article: Distill: Why do we need Flask, Celery, and Redis? (with McDonalds in Between)
- [ ] Article: ML Infrastructure Tools for Data Preparation
- [ ] Article: ML Infrastructure Tools for Model Building
- [ ] Article: ML Infrastructure Tools for Production (Part 1)
- [ ] Article: ML Infrastructure Tools for Production
- [ ] Article: Using Statistical Distances for Machine Learning Observability
- [ ] Article: ML Infrastructure Tools — ML Observability
- [ ] Article: The Model’s Shipped; What Could Possibly go Wrong?
- [ ] Article: The Playbook to Monitor Your Model’s Performance in Production
- [X] Article: Setting Default Docker Environment Variables During Image Build
- [ ] Article: Getting oriented in the RAPIDS distributed ML ecosystem, part 1: ETL
- [ ] Article: Getting oriented in the RAPIDS distributed ML ecosystem, part 2: training and scoring
- [X] Article: Making model training scripts robust to spot interruptions
- [ ] Article: Getting started with large-scale ETL jobs using Dask and AWS EMR
- [ ] Article: Distributed model training using Horovod
- [X] Article: MLOps concepts for busy engineers: model serving
- [X] Article: Enough Docker to be Dangerous
- [X] Article: How Docker Can Help You Become A More Effective Data Scientist
- [X] Article: How to properly ship and deploy your machine learning model
- [X] Article: Docker Explained Visually, For Non-Technical Folks
- [X] Article: A Beginner-Friendly Introduction to Containers, VMs and Docker
- [X] Article: The Playbook to Monitor Your Model’s Performance in Production
- [ ] Blog: Python Speed
- [ ] Connection refused? Docker networking and how it impacts your image
- [ ] Faster or slower: the basics of Docker build caching
- [ ] Where’s your code? Debugging ImportError and ModuleNotFoundErrors in your Docker image
- [ ] A tableau of crimes and misfortunes: the ever-useful docker history
- [ ] Broken by default: why you should avoid most Dockerfile examples
- [ ] A review of the official Dockerfile best practices: good, bad, and insecure
- [ ] The best Docker base image for your Python application (February 2021)
- [ ] A deep dive into the official Docker image for Python
- [ ] Using Alpine can make Python Docker builds 50× slower
- [ ] When to switch to Python 3.9
- [ ] Building on solid ground: ensuring reproducible Docker builds for Python
- [ ] Push and pull: when and why to update your dependencies
- [ ] Installing system packages in Docker with minimal bloat
- [ ] Less capabilities, more security: minimizing privilege escalation in Docker
- [ ] Avoiding insecure images from Docker build caching
- [ ] Build secrets in Docker and Compose, the secure way
- [ ] Security scanners for Python and Docker: from code to dependencies
- [ ] The high cost of slow Docker builds
- [ ] Faster Docker builds with pipenv, poetry, or pip-tools
- [ ] Elegantly activating a virtualenv in a Dockerfile
- [ ] Poetry vs. Docker caching: Fight!
- [ ] Speed up pip downloads in Docker with BuildKit’s new caching
- [ ] Multi-stage builds #1: Smaller images for compiled code
- [ ] Multi-stage builds #2: Python specifics—virtualenv, –user, and other methods
- [ ] Multi-stage builds #3: Why your build is surprisingly slow, and how to speed it up
- [ ] Configuring Gunicorn for Docker
- [ ] Activating a Conda environment in your Dockerfile
- [ ] Shrink your Conda Docker images with conda-pack
- [ ] Reproducible and upgradable Conda environments: dependency management with conda-lock
- [ ] What’s running in production? Making your Docker images identifiable
- [ ] Decoupling database migrations from server startup: why and how
- [ ] A Python prompt into a running process: debugging with Manhole
- [ ] A thousand little details: developing software for ops
- [ ] Your Docker build needs a smoke test
- [ ] Where’s that log file? Debugging failed Docker builds
- [ ] “Let’s use Kubernetes!” Now you have 8 problems
- [ ] Docker BuildKit: faster builds, new features, and now it’s stable
- [ ] Options for packaging your Python code: Wheels, Conda, Docker, and more
- [ ] Docker vs. Singularity for data processing: UIDs and filesystem access

- [X] Cortex Blog
- [X] Server-side batching: Scaling inference throughput in machine learning
- [X] How we served 1,000 models on GPUs for $0.47
- [X] Designing a machine learning platform for both data scientists and engineers
- [X] How to serve batch predictions with TensorFlow Serving
- [X] How to deploy Transformer models for language tasks
- [X] How we scale machine learning model deployment on Kubernetes
- [X] Why we built a serverless machine learning platform—instead of using AWS Lambda
- [X] Why we don’t deploy machine learning models with Flask
- [X] How to deploy machine learning models from a notebook to production
- [X] A/B testing machine learning models in production
- [X] How to deploy 1,000 models on one CPU with TensorFlow Serving
- [X] How to reduce the cost of machine learning inference
- [X] Improve NLP inference throughput 40x with ONNX and Hugging Face
- [X] How to deploy PyTorch Lightning models to production

- [X] Doc: Environment variables in Compose
- [ ] Doc: Lecture 3: Data engineering
- [ ] Luigi Patruno: ML in Production
- [X] Video: You trained a machine learning model. Now what?
- [X] Article: Docker for Machine Learning – Part I
- [X] Article: Docker for Machine Learning – Part II
- [X] Article: Docker for Machine Learning – Part III
- [X] Article: Using Docker to Generate Machine Learning Predictions in Real Time
- [X] Article: Batch Inference vs Online Inference
- [X] Article: Storing Metadata from Machine Learning Experiments
- [X] Article: How Data Leakage Impacts Machine Learning Models
- [ ] Article: An Introduction to Kubernetes for Data Scientists
- [ ] Article: How to Use Kubernetes Pods for Machine Learning
- [ ] Article: Kubernetes Jobs for Machine Learning
- [ ] Article: Kubernetes CronJobs for Machine Learning
- [ ] Article: Kubernetes Deployments for Machine Learning
- [ ] Article: Kubernetes Services for Machine Learning
- [ ] Article: The Ultimate Guide to Model Retraining
- [ ] Article: Top ML Resources: Interview with Eric Colson
- [ ] Article: Top ML Resources: Interview with Veronika Megler, PhD
- [ ] Article: Top ML Resources: Interview with Erik Bernhardsson
- [ ] Article: Top ML Resources: Interview with Rui Carmo
- [ ] Article: Top ML Resources: Interview with Jeremy Jordan
- [X] Article: 5 Challenges to Running Machine Learning Systems in Production
- [ ] Article: Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learning
- [X] Article: What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)
- [X] Article: Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)
- [X] Article: Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)
- [X] Article: The Challenges of Online Inference (Deployment Series: Guide 04)
- [X] Article: Online Inference for ML Deployment (Deployment Series: Guide 05)
- [X] Article: Model Registries for ML Deployment (Deployment Series: Guide 06)
- [X] Article: Test-Driven Machine Learning Development (Deployment Series: Guide 07)
- [X] Article: A/B Testing Machine Learning Models (Deployment Series: Guide 08)
- [X] Article: Lessons Learned from 15 Years of Monitoring Machine Learning in Production
- [X] Article: Why is it Important to Monitor Machine Learning Models?
- [X] Article: Maximizing Business Impact with Machine Learning

- [X] Article: Proxy Metrics
- [X] Article: Celery: an overview of the architecture and how it works
- [ ] Article: Unit Testing Celery Tasks
- [ ] Article: Testing Celery Chains
- [ ] Article: Task Routing in Celery
- [ ] Article: Dynamic Task Routing in Celery
- [ ] Article: Dockerize a Celery app with Django and RabbitMQ
- [ ] Article: How to call a Celery task from another app
- [ ] Article: Distributed Monte Carlo with Celery chords
- [ ] Article: An incredibly simple no-frills Celery setup
- [ ] Article: 3 Strategies to Customise Celery logging handlers
- [ ] Article: Celery task exceptions and automatic retries
- [X] Article: Concurrency and Parallelism
- [ ] Article: Celery, docker and the missing startup banner
- [ ] Article: Monitoring a Dockerized Celery Cluster with Flower
- [ ] Article: Quick Guide: Custom Celery Task Logger
- [ ] Article: Celery on Docker: From the Ground up
- [ ] Article: Kubernetes for Python Developers: Part 1
- [ ] Article: Auto-reload Celery on code changes
- [ ] Book: Building Intelligent Systems: A Guide to Machine Learning Engineering
- [ ] Datacamp: Parallel Computing with Dask
- [X] Datacamp: Data Engineering for Everyone
- [X] Datacamp: Cloud Computing for Everyone
- [X] Pluralsight: Docker and Containers: The Big Picture
- [X] Pluralsight: Docker and Kubernetes: The Big Picture
- [X] Pluralsight: AWS Developer: The Big Picture
- [X] Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)
- [X] Pluralsight: AWS VPC Operations
- [X] Pluralsight: Building Applications Using Elastic Beanstalk
- [ ] Servers for Hackers Series
- [ ] The Hacker's Guide to Scaling Python
- [ ] Udacity: Intro to DevOps
- [X] Udacity: Configuring Linux Web Servers
- [ ] Udacity: Scalable Microservices with Kubernetes
- [X] Udemy: AWS Concepts
- [X] Udemy: Serverless Concepts
- [X] Udemy: AWS Certified Developer - Associate 2018
- [X] Whitepaper: Architecting for the Cloud AWS Best Practices
- [X] Whitepaper: AWS Well-Architected Framework
- [X] Whitepaper: AWS Security Best Practices
- [X] Whitepaper: Blue/Green Deployments on AWS
- [X] Whitepaper: Microservices on AWS
- [X] Whitepaper: Optimizing Enterprise Economics with Serverless Architectures
- [X] Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS
- [X] Whitepaper: Running Containerized Microservices on AWS
- [X] Whitepaper: Serverless Architectures with AWS Lambda
- [X] Youtube: Deploying a machine learning model to the cloud using AWS Lambda
- [X] Youtube: Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models
- [X] Youtube: Human-Centric Machine Learning Infrastructure @Netflix
- [ ] Youtube: Instrumentation, Observability & Monitoring of Machine Learning Models
- [ ] Youtube: OpML '20 - How ML Breaks: A Decade of Outages for One Large ML Pipeline
- [ ] Youtube: Applied ML in Production
- [X] Objective · Applied ML in Production
0:02:38

- [X] Solution · Applied ML in Production
0:08:53

- [X] Evaluation · Applied ML in Production
0:04:02

- [X] Iteration · Applied ML in Production
0:04:35

- [X] Annotation · Applied ML in Production
0:14:34

- [X] Exploratory Data Analysis (EDA) · Applied ML in Production
0:09:02

- [X] Objective · Applied ML in Production
- [ ] MLOps Community Talks
- [X] Our 1st MLOps Meetup - Luke Marsden
0:56:11

- [ ] MLOps Community Meetup #3: Hierarchy of Machine Learning Needs with Phil Winder
0:58:25

- [ ] MLOps Community March 25 2020 featuring Charles Radclyffe
1:03:42

- [ ] MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
0:55:42

- [ ] MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
1:01:35

- [ ] MLOps meetup #5 High Stakes ML with Flavio CLesio
0:55:27

- [ ] MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
0:56:17

- [ ] #8 Optimizing your ML workflow with kubeflow 1.0
1:03:41

- [ ] #10 MLOps the Elephant and the Blind Men with Saurav Chakravorty
0:55:02

- [ ] #11 Machine Learning at scale in Mercado Libre with Carlos de la Torre
0:59:28

- [ ] MLOps meetup #12 // Why data scientists should know data engineering with Dan Sullivan
0:58:28

- [X] #13 Maximizing job opportunities as a data scientists with Anthony Kelly
0:58:24

- [ ] MLOps #14: Kubeflow vs MLflow with Byron Allen
0:54:57

- [ ] MLOps #15 - Scaling Human in the Loop Machine Learning with Robert Munro
0:55:04

- [ ] #16 Venture Capital In Machine Learning Startups with John Spindler
1:05:41

- [ ] MLOps #17 // The challenges of ML Operations and how Hermione helps along the way
1:01:30

- [ ] MLOps #18 // Nubank - Running a fintech on ML
0:53:19

- [ ] MLOps #19 // DataOps and Data versioning in ML
1:01:55

- [ ] MLOps #21 Build vs Buy an ML platform // Diego Oppenheimer - CEO Algorithmia
0:57:20

- [ ] MLOps #22 Deep Dive on Paperspace Tooling // Misha Kutsovsky - Senior ML Architect at Paperspace
1:07:15

- [ ] Feature Stores: An essential part of the ML stack to build great data / Kevin Stumpf - CTO at Tecton
1:05:46

- [ ] MLOps #24 Monitoring the ML stack // Lina Weichbrodt
0:55:32

- [ ] MLOps #25 - How to become a better data scientist: the definite guide // Alexey Grigorev
1:00:42

- [ ] MLOps #26 Python and Dask: Scaling the DataFrame // Dan Gerlanc - Founder of Enplus Advisors
1:28:56

- [ ] #27 How to Leverage ML Tooling Ecosystem Mariya Davydova Head of Product at Neu ro
0:55:57

- [ ] MLOps #28 ML Observability // Aparna Dhinakaran - Chief Product Officer at Arize AI
0:55:04

- [ ] MLOps #29 Continuous Evaluation & Model Experimentation // Danny Ma - Founder of Sydney Data Science
1:00:46

- [ ] MLOps Coffee Sessions #6 Continuous Integration for ML // Featuring Elle O'Brien
1:01:46

- [ ] MLOps #30 Scaling ML Capabilities in Large Organizations // Bertjan Broeksema & Axel Goblet
1:02:47

- [ ] MLOps #31 Path to Production and Monetizing Machine Learning // Vin Vashishta - Data Scientist
0:56:35

- [ ] MLOps #32 Creating Beautiful Ambient Music with Google Brain’s Music Transformer // Daniel Jeffries
0:55:52

- [ ] MLOps #33 Building Say Less: An AI-Powered Summarization App // Yoav Zimmerman - Model Zoo
0:53:08

- [ ] MLOps #34 Owned By Statistics: How Kubeflow & MLOps Can Help Secure ML Workloads // David Aronchick
0:56:18

- [ ] MLOps #35: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent
0:52:50

- [ ] Bring Your On-Prem ML Use Cases to Production on Google Cloud using Kubeflow
0:20:28

- [ ] Moving deep learning from research to prod using DeterminedAI and Kubeflow // David Hershey
0:56:06

- [ ] MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3
1:06:28

- [ ] MLOps Coffee Sessions #14 Conversation with the creators of Dask // Hugo Bowne and Matthew Rocklin
0:56:27

- [ ] When You Say Data Scientist Do You Mean Data Engineer? Lessons Learned From Start Up Life
1:00:43

- [ ] Scalable Python for Everyone, Everywhere // Matthew Rocklin // MLOps Meetup #37
0:57:07

- [ ] Operationalize Open Source Models with SAS Open Model Manager // Ivan Nardini // MLOps Meetup #39
0:56:53

- [ ] Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16
0:57:05

- [ ] Hands-on serving models using KFserving // Theofilos Papapanagiotou // MLOps Meetup #40
0:57:40

- [ ] Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production
0:47:23

- [ ] Metaflow: Supercharging Our Data Scientist Productivity // Ravi Kiran Chirravuri // MLOps Meetup #41
1:00:30

- [ ] UN Global Platform // Mark Craddock // Co-Founder & CTO, Global Certification // MLOps Meetup #42
0:58:48

- [ ] The Current MLOps Landscape // Nathan Benaich & Timothy Chen // MLOps Meetup #43
0:58:31

- [ ] A Conversation with Seattle Data Guy // Benjamin Rogojan // MLOps Coffee Sessions #21
0:47:11

- [X] Our 1st MLOps Meetup - Luke Marsden
- [X] Youtube: Josh Wills: Visibility and Monitoring for Machine Learning Models
- [X] Youtube: PyData Vancouver meetup: cortex.dev : Serving machine learning models in production
- [X] Youtube: Why Your Web Server Should Log to Stdout (Especially with Docker)
- [X] Youtube: What is Load Balancing?
- [X] Youtube: What is Consistent Hashing and Where is it used?
- [X] Youtube: What is a Message Queue and Where is it used?
- [X] Youtube: Why do Databases fail? AntiPatterns to avoid!
- [X] Youtube: What is a microservice architecture and it's advantages?
- [ ] Youtube: What is the Publisher Subscriber Model?
- [ ] Youtube: What's an Event Driven System?
- [ ] Youtube: 5 Tips for System Design Interviews
- [ ] Youtube: How to avoid a single point of failure in distributed systems
- [ ] Youtube: System Design: Tinder as a microservice architecture
- [ ] Youtube: System Design Basics: Horizontal vs. Vertical Scaling
- [ ] Youtube: What is Database Sharding?
- [ ] Youtube: A friendly introduction to System Design
- [ ] Youtube: Avoid cascading failures in a distributed system
- [ ] Youtube: Designing Instagram: System Design of News Feed
- [ ] Youtube: Whatsapp System Design: Chat Messaging Systems for Interviews
- [ ] Youtube: Introduction to NoSQL databases
- [ ] Youtube: Distributed Consensus and Data Replication strategies on the server
- [ ] Youtube: What is an API and how do you design it?
- [ ] Youtube: What is Distributed Caching? Explained with Redis!
- [ ] Youtube: Service discovery and heartbeats in micro-services
- [ ] Youtube: Relational database index vs. NoSQL index
- [ ] Youtube: How Netflix onboards new content: Video Processing at scale
- [ ] Youtube: How to start with distributed systems? Beginner's guide to scaling systems.
- [ ] Youtube: Capacity Estimation: How much data does YouTube store daily?
- [ ] Youtube: How databases scale writes: The power of the log
- [ ] Youtube: System design : Design Autocomplete or Typeahead Suggestions for Google search

- [X] Article: Multi-Armed Bandit (MAB) – A/B Testing Sans Regret
- [X] Article: When to Run Bandit Tests Instead of A/B/n Tests
- [ ] Datacamp: Customer Analytics & A/B Testing in Python
- [ ] Udacity: A/B Testing
- [ ] Udacity: A/B Testing for Business Analysts
- [ ] Youtube: Hypothesis testing with Applications in Data Science
0:10:33

- [ ] Article: No Really, Python's Pathlib is Great
- [X] Article: A deep dive on Python type hints
- [X] Article: I wish I knew these things when I learned Python
- [X] Article: Python Concurrency: The Tricky Bits
- [ ] Article: The Complete Python Development Guide
- [ ] Article: Hypermodern Python
- [ ] Article: Hypermodern Python Chapter 2: Testing
- [ ] Article: Hypermodern Python Chapter 3: Linting
- [ ] Article: Hypermodern Python Chapter 4: Typing
- [ ] Article: Hypermodern Python Chapter 5: Documentation
- [ ] Article: Hypermodern Python Chapter 6: CI/CD
- [ ] Article: Speeding Up Python with Concurrency, Parallelism, and asyncio
- [ ] Article: Speed Up Your Python Program With Concurrency
- [X] Regex For Noobs (like me!) - An Illustrated Guide
- [X] Book: A Byte of Python
- [X] Book: Learn Python The Hard way
- [ ] Book: Python 201
- [ ] Book: Python Anti-Patterns
- [ ] Book: Real Python
- [ ] Book: The Python 3 Standard Library By Example
- [ ] Book: Writing Idiomatic Python 3
- [X] Calmcode: logging
- [X] Calmcode: tqdm
- [X] Calmcode: virtualenv
- [X] Calmcode: ray
- [X] Codecademy: Learn Python
- [X] Cognitiveclass.ai: Python for Data Science
- [ ] Doc: Python Lifecycle Training
- [X] Datacamp: Python for R Users
- [X] Datacamp: Python for Spreadsheet Users
- [X] Datacamp: Importing Data in Python (Part 1)
- [X] Datacamp: Intermediate Python for Data Science
- [X] Datacamp: Python Data Science Toolbox (Part 1)
- [X] Datacamp: Python Data Science Toolbox (Part 2)
- [X] Datacamp: Intro to Python for Finance
- [X] Datacamp: Writing Efficient Python Code
- [X] Datacamp: Writing Functions in Python
- [ ] Datacamp: Working with Dates and Times in Python
- [X] Datacamp: Object-Oriented Programming in Python
- [X] edX: Introduction to Python for Data Science
- [X] edX: Programming with Python for Data Science
- [X] Google's Python Class
- [X] Treehouse: Python Basics
- [X] TheNewBoston: Python Programming Tutorials
- [ ] Udacity: Introduction to Python Programming
- [ ] Udacity: Programming Foundations with Python
- [X] Youtube: Python 3 Programming Tutorial - Regular Expressions / Regex with re
- [X] Youtube: Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
- [ ] Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit
- [ ] Youtube: Python Concurrency and Multithreading
- [ ] Youtube: Aaron Richter- Parallel Processing in Python| PyData Global 2020
- [ ] Youtube: The Clean Architecture in Python

- [ ] Book: Refactoring UI
- [X] Codecademy: Learn HTML
- [X] Codecademy: Learn SASS
- [X] Codecademy: Make a website
- [X] Codecademy: Learn ReactJS: Part I
- [X] Codecademy: Learn ReactJS: Part II
- [X] Codecademy: Learn JavaScript
- [X] Codecademy: Jquery Track
- [X] Codecademy: Learn Ruby
- [X] Code School: Fundamentals of Design
- [X] Code School: Blasting Off with Bootstrap
- [X] (ES6) - Beau teaches JavaScript
- [X] Pluralsight: UX Fundamentals
- [X] Pluralsight: HTML, CSS, and JavaScript: The Big Picture
- [X] Pluralsight: CSS Positioning
- [X] Pluralsight: Introduction to CSS
- [X] Pluralsight: CSS: Specificity, the Box Model, and Best Practices
- [X] Pluralsight: CSS: Using Flexbox for Layout
- [X] Pluralsight: Using The Chrome Developer Tools
- [ ] Thoughtbot: Design for Developers
- [X] Treehouse: HTML
- [X] Treehouse: Javascript Booleans
- [X] Udacity: ES6 - JavaScript Improved
- [X] Udacity: Intro to Javascript
- [X] Udacity: Object Oriented JS 1
- [X] Udacity: Object Oriented JS 2
- [X] Udemy: Understanding Typescript

- [X] Article: Asymptotic Analysis Explained with Pokémon: A Deep Dive into Complexity Analysis
- [X] Book: Grokking Algorithms
- [X] Codecademy: Big O
- [ ] Crashcourse: Computer Science
- [ ] Khan Academy: Data Structures
- [ ] Udacity: Intro to Algorithms
- [ ] Udacity: Intro to Computer Science
- [ ] Udacity: Intro to Theoretical Computer Science
- [ ] Udacity: Programming Languages
- [ ] Udacity: Networking for Web Developers

- [X] Pluralsight: Security Awareness: Basic Concepts and Terminology
- [X] Pluralsight: Secure Software Development
- [X] Pluralsight: Clean Architecture: Patterns, Practices, and Principles
- [ ] Thoughtbot: Software Development Process
- [ ] Thoughtbot: Refactoring
- [ ] Udacity: Design of Computer Programs
- [ ] Udacity: Product Design
- [ ] Udacity: Rapid Prototyping
- [ ] Udacity: Software Development Process

- [X] Article: Work remotely with PyCharm, TensorFlow and SSH
- [X] Article: Python remote debugging with PyCharm, CUDA, and Conda
- [X] Article: How To Use Visual Studio Code for Remote Development via the Remote-SSH Plugin
- [X] Article: Docker as Remote Interpreter for PyCharm Professional
- [ ] Mastering Pycharm
- [ ] Video: Pycharm Tips
- [ ] Youtube: Productive pytest with PyCharm
- [X] Youtube: Getting Started with Python in Visual Studio Code
- [ ] Youtube: 42 PyCharm Tips and Tricks
- [ ] Youtube: Pycharm Quick Tips & Tricks
- [ ] How to use Live Edit to edit HTML&CSS in PyCharm
0:00:49

- [ ] How to let PyCharm install and generate the imports while you write a symbol
0:00:35

- [ ] How to enforce One Import Per Line in PyCharm
0:00:39

- [ ] How to optimize imports in PyCharm
0:01:15

- [ ] How to use the navigation bar to move around your project tree in PyCharm
0:01:09

- [ ] How to use Recent Files to open the tool window in PyCharm
0:00:43

- [ ] How to navigate to symbol using the keyboard in PyCharm
0:00:41

- [ ] How to navigate the cursor position back and forth in PyCharm
0:00:48

- [ ] How to use the 'find action' shortcut in PyCharm
0:01:40

- [ ] How to activate the navigation bar in PyCharm
0:01:36

- [ ] How to navigate to file using the keyboard in PyCharm
0:00:58

- [ ] How to open a file using the keyboard in PyCharm
0:01:03

- [ ] How to use speed search to navigate files in PyCharm
0:01:07

- [ ] How to activate the navigation bar and create a file in PyCharm
0:01:09

- [ ] How to use the find in path dialog in PyCharm
0:00:50

- [ ] How to use drag-n-drop to create a SQLite database in PyCharm
0:00:40

- [ ] How to evaluate expressions during debugging in PyCharm
0:01:13

- [ ] How to add conditions to your breakpoints in PyCharm
0:01:33

- [ ] How to use refactor to rename a file and its references in PyCharm
0:01:06

- [ ] How to quickly view parameter information in PyCharm
0:00:38

- [ ] How to view arguments and documentation without interrupting your flow
0:01:08

- [ ] How to make and extend selections in PyCharm
0:00:39

- [ ] How to reformat your code in PyCharm
0:00:46

- [ ] How to smart-add a new line in PyCharm
0:01:03

- [ ] How to split the screen in PyCharm
0:01:35

- [ ] How to get PyCharm adding fields in a constructor for you
0:00:47

- [ ] How to use refactoring to rename symbols in PyCharm
0:01:13

- [ ] How to avoid disasters by using the local history in PyCharm
0:01:21

- [ ] How to run your project from the keyboard in PyCharm
0:01:21

- [ ] How to disable tabs in PyCharm
0:00:55

- [ ] How to speed up coverage in PyCharm
0:01:34

- [ ] How to enable auto-run for your tests in PyCharm
0:01:45

- [ ] How to run a single test in PyCharm
0:01:08

- [ ] How to use the gutter to quickly spot missing tests in PyCharm
0:01:06

- [ ] How to reword a commit message in PyCharm
0:00:49

- [ ] How to do partial commits in PyCharm
0:01:01

- [ ] How to put a project under version control in PyCharm
0:01:08

- [ ] How to get started with a repository from GitHub in PyCharm
0:01:07

- [ ] How to undo the last commit in PyCharm
0:00:52

- [ ] How to quickly show your npm scripts in PyCharm
0:01:25

- [ ] How to use 'surround with' to insert elements in PyCharm
0:01:11

- [ ] How to use Live Edit to edit HTML&CSS in PyCharm

- [ ] Google: Technical Writing
- [X] Book: Emotional Intelligence
- [X] Book: How to Win Friends & Influence People
- [X] Book: Influence: The Psychology of Persuasion
- [X] Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't
- [X] Book: Multipliers: How the Best Leaders Make Everyone Smarter
- [X] Book: Soft Skills: The software developer's life manual
- [X] Book: The New One Minute Manager
- [X] Calmcode: Remote Work
- [X] Youtube: Building a psychologically safe workplace | Amy Edmondson | TEDxHGSE

- [X] Article: What You Need to Know Before Considering a PhD
- [X] Article: Advice to aspiring data scientists: start a blog
- [ ] Article: Systems Design Interview Guide
- [ ] Article: A Guide to Cold Emailing
- [ ] Book: Machine Learning Systems Design
- [ ] Datacamp: Preparing for Statistics Interview Questions in Python
- [X] Datacamp: Practicing Machine Learning Interview Questions in Python
- [X] Datacamp: Kaggle Competition
- [X] Udacity: Optimize your GitHub
- [X] Udacity: Strengthen Your LinkedIn Network & Brand
- [X] Udacity: Data Science Interview Prep
- [X] Udacity: Full-Stack Interview Prep
- [ ] Udacity: Refresh Your Resume
- [ ] Udacity: Craft Your Cover Letter
- [ ] Udacity: Technical Interview
- [ ] Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers
- [X] Youtube: The Importance of Writing in a Tech Career - Eugene Yan
- [X] Youtube: How to prepare for Machine Learning interviews- Part 1 | Applied AI Course
- [X] Youtube: How to prepare for Machine Learning interviews- Part 2 | Applied AI Course

- [X] Book: Atomic Habits
- [X] Book: Deep Work
- [X] Book: Outliers: The Story of Success
- [X] Book: Platform: The Art and Science of Personal Branding
- [X] Book: Rich Dad Poor Dad
- [X] Book: The Power of Broke
- [X] Book: The 10X Rule
- [X] Book: The Millionaire Fastlane
- [X] Book: The Subtle Art of Not Giving a F**k
- [X] Calmcode: Pomodoro
- [X] Youtube: Why specializing early doesn't always mean career success | David Epstein
- [X] Youtube: Chamath Palihapitiya, Founder and CEO Social Capital, on Money as an Instrument of Change
- [X] Youtube: How to Build a Personal Monopoly with Jack Butcher
- [X] Youtube: How to Use Twitter
- [X] Youtube: A. Jesse Jiryu Davis - Write an Excellent Programming Blog - PyCon 2016
- [X] Youtube: The Great ML Stagnation (Mark Saroufim and Dr. Mathew Salvaris)
- [X] Youtube: What Machine Learning Can Teach Us About Life: 7 Lessons - Talk Python Live Stream