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FisherKK
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Repository for storing and tracking my self-study progress.

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Kamil Krzyk - My Road to AI

About

This is a repository that I have created to track my progress in AI/Data Science related topics in order to organise my knowledge and goals. Purpose of doing this is self-motivation, open source/study material for others, portfolio and TODO list.

Table of contents

AI Related Presentations

| Presentation | Where | Date | Slides | | :---: | :---: | :---: | :---: | | Welcome to MOOC era! - My experiences with Deep Learning Foundations Nanodegree at Udacity | Speaker - GDG & Women Techmakers - Machine Learning #3 | 18.10.2017 | Link | | Soft introduction into MSE based Linear Regression (part 2 of 'What this Machine Learning is all about?' talk) | Azimo Lunch & Learn | 16.11.2017 | Link | | Advantages of Batch Normalization in Deep Learning | PyData Warsaw, Let’s meet to talk about AI in Bialystok #2 | 10.04.2018, 01.06.2018 | Link |

Mini AI Projects

In this section I will focus about digging up relationship and visualising data. I will try to use Machine learning and visualisation methods for problem solving.

Problem Solving

| Problem | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | | Prediction of Bike Shop Clients Number | Used MLP with 1-layer, mini-batch | Python (numpy, matplotlib) | Bike-Sharing | 13.08.2017 | 13.08.2017 | | Kaggle - Titanic Disaster survivor prediction | Used Logistic Regression with ~80% accuracy | Python (raw) | Titanic Disaster | 19.10.2017 | 24.10.2017 |

Visualisation

| Problem | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | | Picking best computer game to try| Used K-Means Clusters for visualising top positions | Python (raw) | Kaggle - Video Game Sales | 01.10.2017 | 05.10.2017 |

AI Programming Showcase

In this section I want to show off my knowledge about various AI related algorithms, frameworks, programming languages, libraries and more. Priority is to show how the algorithm works - not to solve complex and ambitious problems. Usually on classical or generated datasets.

Raw Python

Machine Learning

| Algorithm | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | | Linear Regression | - | Python (raw) | Generated Numbers | 18.04.2017 | 15.09.2017 | | Ridge Regression | Compared result with Linear Regression | Python (raw) | Generated Numbers | 23.09.2017 | 23.09.2017 | | Polynomial Regression | Approximating Polynomial of degree 2 | Python (raw) | Generated Numbers | 08.06.2017 | 15.09.2017 | | Polynomial Regression | Approximating Polynomial of degree 3 | Python (raw) | Generated Numbers | 10.06.2017 | 15.09.2017 | | KNN | Manhattan, Euclidean Similarity | Python (raw) | iris | 21.07.2017| 24.09.2017 | | PCA | - | Python (raw) | Generated Numbers | 01.04.2017 | 23.09.2017 | | Naive Bayes | Gaussian Distribution | Python (raw) | Pima Indian Diabetes | 02.11.2017 | 03.11.2017 |

Deep Learning

| Net Type | Problem | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | MLP | Digit Classification | 2-layers, mini-batch | Python (raw) | MNIST | 19.06.2017 | 14.08.2017 |

sklearn

| Algorithm | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | | Linear Regression | - | Python (sklearn) | Generated Numbers | 18.04.2017 | 15.09.2017 | | Polynomial Regression | Approximating Polynomial of degree 2 | Python (sklearn) | Generated Numbers | 10.06.2017 | 15.09.2017 | | Polynomial Regression | Approximating Polynomial of degree 3 | Python (sklearn) | Generated Numbers | 10.06.2017 | 15.09.2017 | | KNN | Euclidean Similarity | Python (sklearn) | iris | 22.07.2017 | 24.09.2017 |

TensorFlow

Machine Learning

| Algorithm | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | | Linear Regression | - | Python (Tensorflow) | Generated Numbers | 23.09.2017 | 23.09.2017 |

Deep Learning

| Net Type | Problem | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | MLP | Digit Classification | 2-layers, mini-batch, dropout-regularization | Python (Tensorflow) | MNIST | 29.06.2017 | 18.07.2017 | | MLP | Encrypting data with Autoencoder | 1-layer Encoder, 1-layer Decoder, mini-batch | Python (Tensorflow) | MNIST | 13.07.2017 | 13.07.2017 | | MLP | Digit Classification | tf.layer module, dropout regularization, batch normalization | Python (Tensorflow) | MNIST| 16.08.2017 | 23.08.2017 | | CNN | 10 Classes Color Images Classification | tf.nn module, dropout regularization | Python (Tensorflow) | CIFAR-10 | 16.08.2017 | 07.09.2017 | | CNN | 10 Classes Color Images Classification | tf.layer module, dropout regularization | Python (Tensorflow) | CIFAR-10 | 16.08.2017 | 09.09.2017 | | CNN | 10 Classes Color Images Classification | tf.layer module, dropout regularization, batch normalization | Python (Tensorflow) | CIFAR-10 | 19.08.2017 | 10.09.2017 | | RNN | Simple Language Translator | In form of my DLFND project for now | Python (Tensorflow) | Small part of French-English corpus | 05.05.2017 | 24.05.2017 | | RNN | "The Simpsons" Script Generation | In form of my DLFND project for now | Python (Tensorflow) | "The Simpsons" script | 06.06.2017 | 14.07.2017 | | DCGAN | Generating Human Face Miniatures | DCGAN | Python (Tensorflow) | CelebA | 11.09.2017 | 13.09.2017 |

Keras

| Net Type | Problem | Description | Implementation | Dataset | Creation Date | Last Update | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | MLP | Digit Classification | 2-layers, mini-batch, BN | Python (Keras) | MNIST | 10.03.2018 | 10.03.2018 | | MLP | Clothes Images Classification | 2-layers, mini-batch, BN | Python (Keras) | Fashion MNIST | 15.04.2018 | 15.04.2018 | | MLP | Letters Classification | 2-layers, mini-batch, BN | Python (Keras) | EMNIST | 24.04.2018 | 24.04.2018 | | MLP | Review Sentiment Classification | Bag of Words | Python (Keras) | IMDB Reviews | 11.03.2018 | 11.03.2018 | | MLP | Boston House Prices Regression | 1 layer, mini-batch | Python (Keras) | Boston House Prices | 19.04.2018 | 19.04.2018 | | CNN | Ten Color Image Classes Classification | VGG15 | Python (Keras) | CIFAR10 | 27.03.2018 | 27.03.2018 | | CNN | Letter Classification | 32x32x64x64, 512, BN | Python (Keras) | EMNIST | 25.03.2018 | 23.03.2018 | | CNN | Clothes Images Classification | 16x16x32x32, 256x128, BN | Python (Keras) | Fashion MNIST | 11.03.2018 | 11.03.2018 | | CNN | Digit Classification | 16x32x64, 128, BN| Python (Keras) | MNIST | 24.03.2018 | 24.03.2018 | | RNN | Next Month Prediction | LSTM(128) | Python (Keras) | Month Order | 15.04.2018 | 15.04.2018 | | RNN | Shakespeare Sonnet's Generation | LSTM(700), LSTM(700) | Python (Keras) | Shakespeare's sonnets | 17.04.2018 | 17.04.2018 |

Articles

Note: Delayed due to 1,5 month long preparations for organising ML/DL workshops.

| Title | Link | Jupyter | Publsh Date | Update Date | | :---: | :---: | :---: | :---: | :---: | | Coding Deep Learning for Beginners — Start! | Medium | - | 12.02.2018 | 12.02.2018 |

Courses & Certificates

When I was younger I played a lot of computer games. I still tend to play today a little as a form of relax and to spend time with friends that live far from me. One thing that I have very enjoyed about gaming was gathering trophies. You made an effort to complete list of challenges or get a great score and then looked at list of your achievements with satisfaction. My current self have inherited this habit and as I study on daily basis I like to gather proves that I have done something - to make it more like a game where each topic is a boss that you have to clear on hard mode. Of course what's in your head is most important but if it helps to motivate you, then why not?

Sources

There is a list of sources that I have used (and found helpful in some way) or keep using in order to produce my repo content.

Contact

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