The SDML book club started reading Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann in August 2020. At present, the book is available for free download via the PyTorch website: https://pytorch.org/deep-learning-with-pytorch.
The book comes with a set of Jupyter notebooks so that you can run the code examples in the book. The notebooks for the book are located in the GitHub repository https://github.com/deep-learning-with-pytorch/dlwpt-code. Decide the directory where you would like to store the files on your system and clone or download the repo there.
Here are the chanpter notes and meetup recordings:
Chapter 1: Introducing deep learning and the PyTorch Library
and
Chapter 2: Pretrained networks
Notes and Meetup video
Chapter 3: It starts with a tensor
Notes and Meetup video
Chapter 4: Real-world data representation using tensors
Notes and Meetup video
Chapter 5: The mechanics of learning
Notes and Meetup video
Chapter 6: Using a neural network to fit the data
Notes and Meetup video
Chapter 7: Telling birds from airplanes: Learning from images
Notes and Meetup video
Chapter 8: Using convolutions to generalize
Notes and Meetup video
Recap of Part I (chapters 1-8)
Notes
Chapter 9: Using PyTorch to fight cancer
Notes
Chapter 10: Combining data sources into a unified dataset
Notes
Meetup video for all three of the above
Chapter 11: Training a classification model to detect suspected tumors
Notes and Meetup video
Chapter 12: Improving training with metrics and augmentation
Notes and Meetup video
Chapter 13: Using segmentation to find suspected nodules
Notes and Meetup video
Chapter 14: End-to-end nodule analysis, and where to go next
Notes and Meetup video
Chapter 15: Deploying to production
Notes and Meetup video