Awesome-PyTorch-Chinese

Overview of the Awesome-PyTorch-Chinese Project

The Awesome-PyTorch-Chinese project serves as a comprehensive resource hub for anyone interested in exploring the world of PyTorch, a highly popular deep learning library. This project aims to provide individuals with a wide array of resources focusing on PyTorch tutorials, hands-on projects, recommended papers, and suggested books, all of which are curated with an emphasis on accessibility for Chinese-speaking audiences. Below, you will find various sections that summarize the resources available through this project.

PyTorch Learning Tutorials and Manuals

• Official PyTorch Tutorials (English): These are highly recommended for those proficient in English. The material covers everything from the basics of PyTorch to building deep neural networks, complete with detailed syntax guides and high-quality examples.

• PyTorch Chinese Documentation: For those who find the English version challenging, the official Chinese documentation is available. It's an exhaustive crash course and reference manual for PyTorch's numerous functions.

• Algorithm-Focused PyTorch Code Tutorials: This GitHub repository offers practical PyTorch coding tutorials, with a significant following. It’s advisable to familiarize oneself with the basic tutorials listed above before diving into these examples.

• Open Source PyTorch Handbook: A community-driven resource aimed at those looking to quickly get started with PyTorch for deep learning research and development. Although still growing, it provides an excellent starting point.

• Beginner-Friendly PyTorch Chinese Documentation: This guide is perfect for novices, covering topics from the fundamentals of PyTorch to practical examples like image classifiers and data parallelism.

PyTorch Video Tutorials

• Bilibili PyTorch Video Tutorials: These popular video tutorials on Bilibili are brief yet thorough, targeting viewers who prefer audiovisual learning, albeit without subtitles for practicing English comprehension.

• YouTube Video Tutorials: Another highly-viewed series by an international expert, offering insightful content in English.

• Morvan’s Tutorials: Known for his engaging style, Morvan's tutorials are available on platforms like Bilibili and YouTube.

• AI 101 Academy: Detailed PyTorch tutorials covering a broad spectrum of knowledge, ideal for in-depth learning.

• July Online Courses: Comprehensive paid courses from a leading AI education platform, integrating theory with practical applications in NLP and CV projects.

NLP & PyTorch Practice

• Torchtext: A library for effectively handling text preprocessing, featuring datasets and vocabulary tools.

• Pytorch-Seq2seq: A framework for constructing Seq2seq models for sequence tasks, providing modular and extensible components.

• BERT NER: Implements BERT in PyTorch, a powerful pre-trained language model that's easy to use for various NLP tasks.

• Fairseq: Offers custom models for tasks like translation and summarization, along with implementations of various Seq2seq models.

• Quick-NLP: Inspired by fast.ai, it simplifies the process of running NLP models with a user-friendly API.

• OpenNMT-py: An open-source framework for neural machine translation, designed for research and industry applications.

CV & PyTorch Practice

• Torchvision: A set of tools for image operations, encompassing datasets, models, transformations, and utilities.

• OpenFacePytorch: Implements OpenFace in PyTorch, useful for face alignment processes.

• TorchCV: A comprehensive computer vision framework supporting various visual tasks.

• Pytorch-cnn-finetune: Enables fine-tuning of pre-trained Convolutional Neural Networks like ResNet and DenseNet.

• Pt-styletransfer: Focuses on neural style transfer using PyTorch, with several features for customizable outputs.

• Face-alignment: A library for 2D and 3D face alignment through face landmark detection in Python.

Recommended PyTorch Papers

• Google_evolution: Implements large-scale evolution results for image classifiers from a seminal research paper by Esteban Real et al.

• PyTorch-value-iteration-networks: Illustrates the concept of Value Iteration Networks, highly acclaimed in deep reinforcement learning studies.

• Pytorch Highway: Discusses Highway Networks, influenced by LSTM networks, allowing for seamless information flow across network layers.

• Pyscatwave: Explores scattering networks for visual tasks, reducing input resolution while preserving essential information.

• Pytorch_NEG_loss: Details Negative Sampling Loss, a key concept in neural language model training.

• Pytorch_TDNN: Delves into Time Delayed Neural Networks, detailing their functionality and implementations.

Recommended PyTorch Books

1. "Deep Learning with PyTorch" by Xiaoyu Liao (★★★): A beginner-friendly guide, though it contains some inaccuracies that require careful interpretation.

2. "PyTorch Deep Learning" by Hailing Wang and Jiangfeng Liu (★★★): An English-translated book by esteemed Indian authors, covers advanced neural architectures suitable for experienced data scientists.

3. "PyTorch Framework: Beginners and Practical Guide" by Yun Chen (★★★★): Offers a well-balanced mix of theory and practice, with detailed project examples for deep learning newcomers.

4. "PyTorch Machine Learning from Beginner to Real Projects" by Sunnyline Sun, et al. (★★★): A solid blend of theory and practical projects, excellent for new learners of PyTorch.

This extensive suite of resources within the Awesome-PyTorch-Chinese project is designed to cater to diverse needs, whether it's understanding foundational concepts or pursuing advanced applications in real-world scenarios.