deeplearning_ai_books

This repository is a community-produced Chinese-language set of notes and resources for Andrew Ng's Deep Learning Specialization, the well-known online course series on deeplearning.ai. The course itself is taught in English by Andrew Ng with two teaching assistants from Stanford, and is hosted on Coursera; this project transcribes and translates the videos and subtitles into written Chinese notes so Chinese-speaking learners can study them as a book rather than only by watching the videos. The README explains the scope of the course the notes follow. The Deep Learning Specialization is aimed at people who already have some programming background — comfortable with Python and with a basic understanding of machine learning — and want to break into AI. It is organized into five courses: an introduction to neural networks and deep learning; improving deep neural networks through hyperparameter tuning, regularization and optimization; structuring machine learning projects; convolutional neural networks (CNN) for computer vision; and sequence models including recurrent neural networks (RNN) and long short-term memory (LSTM). The README lists a full table of contents at the week and lesson level, covering topics like logistic regression, gradient descent, vectorization, activation functions, backpropagation, mini-batch gradient descent, Adam optimization, batch normalization, edge detection, pooling layers, residual networks, transfer learning, and end-to-end deep learning. The course uses Python and the TensorFlow framework, and is estimated to take three to four months. The notes were assembled by Huang Haiguang (a PhD) with a long list of contributors and editors credited in the README. They are distributed free of charge and not for commercial use, with links to read them online, on Bilibili, and via the author's Zhihu page and WeChat public account. A companion repository for Andrew Ng's earlier Machine Learning course is also linked.