PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab. This documentation provides a comprehensive guide to understanding and utilizing PyTorch.
Getting Started
- Quick Start - A brief guide to setting up PyTorch and creating your first model.
- Installation Guide - Detailed instructions on installing PyTorch on various platforms.
Core Concepts
- Tensors: The basic data structure in PyTorch for storing multi-dimensional arrays.
- Autograd: An automatic differentiation system that simplifies the computation of derivatives.
- Neural Networks: Tutorials and guides for building and training neural networks.
Resources
- Official PyTorch Website - The central hub for PyTorch documentation, tutorials, and community resources.
- Torchvision - A library of common image transformations and pre-trained models for computer vision.
- TorchText - A library for NLP that provides a suite of text processing tools and datasets.
Tutorials
- Tutorials - Step-by-step guides covering various topics in machine learning with PyTorch.
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By following these resources, you will gain a solid foundation in using PyTorch for your machine learning projects.