nndl/nndl Alternatives

choose for an interactive, code-first textbook with runnable examples; the seed is theory-only and has no public code.

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

choose for a practical, high-level approach using fastai, if you prefer learn-by-building over theory.

The fastai book, published as Jupyter Notebooks

choose if you prefer a video course + notebooks over a formal textbook; it's PyTorch-specific and actively maintained.

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

choose for MIT-style lecture labs with hands-on exercises, not a comprehensive theory text.

Lab Materials for MIT 6.S191: Introduction to Deep Learning

choose for a Keras-based practical course with real-world applications, not a general theory textbook.

T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

choose for a Chinese tutorial derived from Li Hongyi's lectures, which is more accessible but less formal than the seed.

《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases

choose for a hands-on Chinese introduction with practical projects, if you prefer applied learning over theory.

AI实战-practicalAI 中文版

choose for quick TensorFlow code samples; it is a narrow example collection, not a full textbook.

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

choose for PyTorch-specific hands-on tutorials; it lacks the theoretical depth and comprehensiveness of the seed.

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

choose for concise code snippets across many DL topics; it is a collection, not a structured textbook.

A resource for learning about Machine learning & Deep Learning

choose for the notebooks from Géron's Hands-On ML book; it offers executable examples but not the book's full narrative.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.

choose for a Chinese TensorFlow 2.0-based intro book; it is older and framework-specific compared to the seed.

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.