PyTorch-Tutorial

This repository is a set of worked code examples and Jupyter notebooks for learning PyTorch, a popular Python library for building neural networks. The tutorials start from the basics and progress through increasingly advanced network designs. A companion Chinese-language video and text course is available at the author's website for Mandarin speakers.

The content is organized into four sections. The first covers PyTorch fundamentals: how it relates to NumPy (a numerical computing library), how variables and gradients work, and how to apply activation functions. The second section walks through building your first networks, covering regression (predicting a number), classification (sorting inputs into categories), saving and loading trained models, training in mini-batches, and comparing different optimization strategies.

The third section introduces more advanced architectures: convolutional neural networks (CNNs, commonly used for image tasks), recurrent neural networks (RNNs, useful for sequences like text or time series), autoencoders (networks that learn compressed representations of data), generative adversarial networks (GANs, which generate new data that resembles a training set), and reinforcement learning approaches. The fourth section covers practical techniques like dropout (a method to reduce overfitting), batch normalization, and running training on a GPU.

Each topic has a standalone Python script you can run directly, and many also have Jupyter notebook versions contributed by a community collaborator. The tutorials assume some familiarity with Python but explain each neural network concept as it appears in the code.