eat_pytorch_in_20_days

Why we included this project

This is a notebook-based PyTorch course for people who already know the basics of machine learning and have trained a simple model before. Rather than mirroring the official docs, it organizes the material around practical modeling workflows, so each chapter works through a complete example for one data type, from structured tables to images, text, or time series, before covering core concepts like tensors, autograd, and dynamic computation graphs. The middle sections map to PyTorch's low-, mid-, and high-level APIs, and a chapter on recommendation and advertising models (FM, DeepFM, DIN, DIEN) gives engineers a concrete reference when building recommenders. The examples are deliberately small and self-contained, so much of the code can be lifted and adapted for real projects. For teams onboarding newcomers, it is a gentler entry point than reading framework documentation cold, and the 20-day plan is easy to fit around a normal job.