16.8K ·GitHub
What is this?
A free Chinese-language deep learning tutorial (nicknamed the 'Apple Book') based on Prof. Li Hongyi's (Hung-yi Lee) 'Machine Learning' course, covering most major deep learning topics in detail.
A free Chinese-language deep learning tutorial (nicknamed the 'Apple Book') based on Prof. Li Hongyi's (Hung-yi Lee) 'Machine Learning' course, covering most major deep learning topics in detail.Is this a written book, or does it come with runnable code?It is mainly a written tutorial/book with detailed derivations, but it also ships companion Jupyter notebook code for the topics it covers.
It is mainly a written tutorial/book with detailed derivations, but it also ships companion Jupyter notebook code for the topics it covers.Where should a beginner start, and how is the content organized?Chapters run progressively from an introduction and deep-learning basics, through CNNs, self-attention, RNNs and Transformers, to generative models, self-supervised learning, and finally ChatGPT, so it is meant to be read in order.
Chapters run progressively from an introduction and deep-learning basics, through CNNs, self-attention, RNNs and Transformers, to generative models, self-supervised learning, and finally ChatGPT, so it is meant to be read in order.Do I need to buy the printed book, or can I just get the PDF for free?A free PDF is available via the project's GitHub Releases (with an extraction code), and a printed edition can optionally be purchased from JD or Dangdang.
A free PDF is available via the project's GitHub Releases (with an extraction code), and a printed edition can optionally be purchased from JD or Dangdang.Can I freely redistribute or reuse the tutorial's content?No explicit open-source license is declared for the content; the authors only ask that reprints credit the original source.
No explicit open-source license is declared for the content; the authors only ask that reprints credit the original source.What's the difference between the free PDF and the printed book?The PDF is the original full draft, while the printed book was further revised and edited by People's Posts and Telecommunications Press before publication.
The PDF is the original full draft, while the printed book was further revised and edited by People's Posts and Telecommunications Press before publication.Does this tutorial cover ChatGPT?Yes, the final chapter addresses common misconceptions about ChatGPT, the pretraining technique behind it, and open research questions it raises.
Yes, the final chapter addresses common misconceptions about ChatGPT, the pretraining technique behind it, and open research questions it raises.What related resources does the project point to for reinforcement learning?It recommends LS-Imagine for RL applied to Minecraft, the 'Mushroom Book' EasyRL for general RL, and Awesome Visual RL for visual reinforcement learning.
It recommends LS-Imagine for RL applied to Minecraft, the 'Mushroom Book' EasyRL for general RL, and Awesome Visual RL for visual reinforcement learning.What deep learning topics does the tutorial cover overall?It spans CNNs, self-attention/Transformers, RNNs, GANs and diffusion models, self-supervised learning (BERT, GPT-3), adversarial attacks, explainable AI, transfer learning, deep RL, lifelong learning, network compression (pruning, distillation), meta-learning, and ChatGPT.
It spans CNNs, self-attention/Transformers, RNNs, GANs and diffusion models, self-supervised learning (BERT, GPT-3), adversarial attacks, explainable AI, transfer learning, deep RL, lifelong learning, network compression (pruning, distillation), meta-learning, and ChatGPT.What are similar projects?DeepLearning, AI-For-Beginners, deeplearning-models, examples, DeepLearning-500-questions, TensorFlow-Examples, ML-NLP, Ai-Learn
DeepLearning, AI-For-Beginners, deeplearning-models, examples, DeepLearning-500-questions, TensorFlow-Examples, ML-NLP, Ai-LearnWhich topics is it in?rnn, explainable-ai, knowledge-distillation, transfer-learning, bert, gan, cnn, diffusion, reinforcement-learning, neural-network, jupyter notebook, chatgpt
rnn, explainable-ai, knowledge-distillation, transfer-learning, bert, gan, cnn, diffusion, reinforcement-learning, neural-network, jupyter notebook, chatgpt