practical-pytorch

This repository is a now-deprecated collection of beginner-friendly tutorials for learning PyTorch, an AI framework used to build and train neural networks. The tutorials have since been folded into the official PyTorch documentation, so new learners are directed there for up-to-date versions. What remains here is the original set of notebooks that once helped many people get their first footing with the library.

The tutorials are organized around practical projects rather than abstract theory. The first series focuses on recurrent neural networks applied to text tasks. One notebook walks through classifying names by language using character-level patterns. Another generates Shakespeare-style text. A third generates names conditioned on a target category. A fourth translates language using a sequence-to-sequence model with an attention mechanism, which is a technique that helps the model focus on the right parts of a sentence when translating. There is also a notebook exploring word vectors using GloVe, a pre-trained set of numerical representations for words.

A second series was planned to cover recurrent networks applied to time-series data, but those notebooks were never finished. The README marks them as work in progress.

To run the notebooks locally, the instructions call for installing Anaconda (a Python distribution bundled with data science tools), then PyTorch, then cloning the repo and launching Jupyter Notebook in a browser. The setup is straightforward for a Linux or Mac machine.

The README also lists external reading resources covering RNN theory, machine translation techniques, and attention models, giving learners a path to go deeper. Because this repo is deprecated and no longer maintained, anyone starting fresh should go directly to the official PyTorch tutorials repository for code that works with current versions of the library.