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NOTICE: These Notebooks are Outdated and have all been moved to https://github.com/walkwithfastai/walkwithfastai.github.io, and the course is live at https://walkwithfastai.com

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.

Helpful Folks:

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.

  • 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.

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Notebooks for the "A walk with fastai2" Study Group and Lecture Series

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