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
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.
- NOTE: This course does not have a certification or credit. This is something I have been doing for the past few semesters to help branch fellow Undergraduates at my school into the world of fastai, and this year I am making it much more available.