A current, comprehensive Chinese deep-learning theory book, directly comparable to the seed and a drop-in replacement.
邱锡鹏《神经网络与深度学习》第二版与通识版:电子书、章节目录、学习资源与勘误。
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deep learning education / tutorial
Chinese deep learning tutorial derived from Li Hongyi's lectures, providing theory, formulas, and code examples.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose for a practical fastai-based introduction with runnable notebooks, but it is less theory-focused and stale (no push in over 18 months).
The fastai book, published as Jupyter Notebooks
Choose for hands-on labs and video lectures, but it is a course supplement rather than a self-contained textbook.
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Choose for a practical, hands-on Chinese course, but it focuses on application more than theory.
AI实战-practicalAI 中文版
Choose for an interactive, executable deep-learning book, but note it has not been updated in over 18 months.
《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 video-and-code examples across ML/DL, but it lacks the structured theoretical narrative of the seed.
A resource for learning about Machine learning & Deep Learning
Pick for detailed university-style Jupyter notebooks on deep learning, but it is in English and code-oriented.
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
Use for notebooks accompanying Hands-On ML, good for scikit-learn/PyTorch practice, but not a Chinese theory book.
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.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Use only for concise TensorFlow code snippets; it is framework-specific and outdated.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Choose if you need a beginner PyTorch tutorial in Chinese, but it lacks the theoretical depth and breadth of the seed.
Build your neural network easy and fast, 莫烦Python中文教学
Use for Keras-focused applied deep learning, but it is narrower in theory and framework choice.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Pick for a project-based PyTorch course, but it does not provide the comprehensive theoretical coverage of the seed.
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Aged TensorFlow tutorial series, now outdated and not a general deep-learning reference.
:satellite: Simple and ready-to-use tutorials for TensorFlow
Old TensorFlow Chinese tutorial, far behind current practice and not a comprehensive DL theory source.
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
PyTorch-specific handbook, stale and limited to that framework.
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
These projects were analysed and named leedl-tutorial among their alternatives. The relationship is not symmetric — how leedl-tutorial rates them is a separate judgement, made when leedl-tutorial is analysed in its own right.
calls leedl-tutorial “choose when you want lecture-aligned Chinese deep-learning notebooks with more theory than 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
calls leedl-tutorial “For a Chinese tutorial based on a popular deep learning course.”
A resource for learning about Machine learning & Deep Learning
calls leedl-tutorial “Choose it for a recent, theory-first Chinese PyTorch course covering deep learning fundamentals.”
Build your neural network easy and fast, 莫烦Python中文教学
calls leedl-tutorial “choose it for a Chinese-language deep learning textbook/course based on Hung-yi Lee's lectures.”
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
calls leedl-tutorial “Choose for an up-to-date, Chinese-language deep learning course with PyTorch, including theory and code.”
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
calls leedl-tutorial “Choose for a Chinese-speaking deep-learning tutorial with PyTorch, not general NumPy ML.”
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
calls leedl-tutorial “choose for a Chinese tutorial derived from Li Hongyi's lectures, which is more accessible but less formal than the seed.”
邱锡鹏《神经网络与深度学习》第二版与通识版:电子书、章节目录、学习资源与勘误。
calls leedl-tutorial “Use for a Chinese-language deep learning course with lecture-aligned notebooks, mostly in PyTorch.”
:satellite: Simple and ready-to-use tutorials for TensorFlow
calls leedl-tutorial “Choose for a comprehensive university-style deep learning course (Chinese) that includes NLP among many topics.”
Natural Language Processing Tutorial for Deep Learning Researchers
calls leedl-tutorial “When you want a comprehensive, well-structured deep learning tutorial based on Hung-yi Lee's course, rather than the seed's multi-topic course fragments.”
A collection of machine learning examples and tutorials.
calls leedl-tutorial “Choose when you want Chinese-language notes following Hung-yi Lee's DL lectures rather than MIT labs.”
Lab Materials for MIT 6.S191: Introduction to Deep Learning
calls leedl-tutorial “Chinese deep-learning tutorial (Hung-yi Lee), narrower than the seed's full ML/DL/NLP coverage.”
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。
calls leedl-tutorial “When you are learning deep learning in Chinese and specifically want Li Hongyi's course notebooks rather than the broader ML+DL seed.”
AI实战-practicalAI 中文版