yunjey/pytorch-tutorial

This repository is a collection of tutorial code designed to teach deep learning researchers and practitioners how to use PyTorch, a popular open-source machine learning framework. The problem it solves is straightforward: PyTorch is powerful but has a learning curve, and this project offers concise, working examples that cover the most common model types, all in fewer than 30 lines of code each, making the concepts approachable rather than overwhelming. The tutorials are organized into four levels. The basics section introduces fundamental building blocks: tensor operations (PyTorch's core data structure, similar to NumPy arrays), linear regression (predicting a continuous value from input data), logistic regression (binary classification), and simple feedforward neural networks. The intermediate section steps up to convolutional neural networks (used for image recognition), recurrent neural networks (designed for sequential data like text or time series), and language models. The advanced section tackles more sophisticated techniques like generative adversarial networks (two neural networks competing to generate realistic data), variational autoencoders (a type of generative model), neural style transfer (applying one image's artistic style to another), and image captioning (combining vision and language). A utilities section also covers TensorBoard integration for visualizing training metrics. Someone would use this repository when starting out with PyTorch and wanting to see real, runnable code for each model type rather than reading abstract documentation. It is also useful as a quick reference for experienced practitioners who want a clean implementation of a specific architecture. Each tutorial is self-contained and can be run directly. The stack is Python (2.7 or 3.5+) and PyTorch 0.4.0 or later, with no additional complex dependencies.