The PyTorch book that admits it skips RNNs

What it does This repository contains the Jupyter notebooks and code accompanying Manning’s Deep Learning with PyTorch. It walks developers through foundational deep learning concepts using PyTorch, culminating in a real-life project. The authors explicitly position it as a conceptual companion rather than a reference manual, meaning it teaches you enough to go explore the advanced corners of the framework on your own.

The interesting bit The authors include two PyTorch core developers, so the explanations of what happens “behind the curtain” come from people who actually hung that curtain. The book also refreshingly admits its own boundaries: it skips recurrent neural networks and other API corners entirely, choosing depth on a subset of features over breadth.

Key highlights

• Authored by Eli Stevens, Luca Antiga, and Thomas Viehmann; Antiga and Viehmann are PyTorch core contributors.
• Targets developers and students comfortable with Python and basic linear algebra, with no prior deep learning knowledge assumed.
• Focuses on building intuition for how PyTorch implements foundational concepts rather than cataloging every API flag.
• Explicitly omits RNNs and other API areas, treating the book as a launchpad for independent exploration rather than an encyclopedia.
• Maintains an errata page on GitHub Pages for post-publication corrections.

Caveats

• The code covers only a subset of PyTorch; if you need RNNs or full API coverage, you will need supplementary material.
• As a companion to a published book, the repository is primarily educational scaffolding rather than a standalone library or application.
• The README does not describe the specific “real-life project” in detail, so it is unclear whether the notebooks stand alone without the text.

Verdict Grab this if you are a Python-literate developer or student looking for a curated, opinionated path into PyTorch fundamentals. Skip it if you are after a comprehensive API reference or need production-ready recurrent neural network examples.