Notebooks for the "A walk with fastai2" Study Group and Lecture Series
This course will run from January 15th until May and will be live-streamed on YouTube. Each lecture will be between an hour to an hour and 15 minutes, followed by an hour to work on projects related to the course.
Requirements: * A Google account to utilize Google Colaboratory * A Paperspace account for Natural Language Processing
YouTube Channel with Lectures: Click Here
The overall schedule is broken up into blocks as such:
BLOCKS: * Block 1: Computer Vision * Block 2: Tabular Neural Networks * Block 3: Natural Language Processing
Here is the overall schedule broken down by week: This schedule is subject to change
Block 1 (January 15th - March 4th): * Lesson 1: PETs and Custom Datasets (a warm introduction to the DataBlock API) * Lesson 2: Image Classification Models from Scratch, Stochastic Gradient Descent, Deployment, Exploring the Documentation and Source Code * Lesson 3: Multi-Label Classification, Dealing with Unknown Labels, and K-Fold Validation * Lesson 4: Image Segmentation, State-of-the-Art in Computer Vision * Lesson 5: Style Transfer,
nbdev, and Deployment * Lesson 6: Keypoint Regression and Object Detection * Lesson 7: Pose Detection and Image Generation * Lesson 8: Audio
Block 2 (March 4th - March 25th): * Lesson 1: Pandas Workshop and Tabular Classification * Lesson 2: Feature Engineering and Tabular Regression * Lesson 3: Permutation Importance, Bayesian Optimization, Cross-Validation, and Labeled Test Sets * Lesson 4: NODE, TabNet, DeepGBM
BLOCK 3 (April 1st - April 22nd): * Lesson 1: Introduction to NLP and the LSTM * Lesson 2: Full Sentiment Classification, Tokenizers, and Ensembling * Lesson 3: Other State-of-the-Art NLP Models * Lesson 4: Multi-Lingual Data, DeViSe
We have a Group Study discussion here on the Fast.AI forums for discussing this material and asking specific questions.