About this project

This is a collection of deep learning study notes for Chinese learners. The author has organized notes from multiple public video courses into numbered chapters for sequential self-study. Main content sources and chapter divisions: - PyTorch tutorial (instructor: Tu Dui), notes numbered 100-122, focusing on PyTorch basics and introductory practice. - Deep learning tutorial (instructor: Li Mu), notes numbered 200-268, corresponding to the 'Hands-On Deep Learning' video series. - Deep learning tutorial (instructor: Andrew Ng), notes numbered 300-354, corresponding to his deep learning course. - Large model Agent tutorial (instructor: Da Fei), notes numbered 400-409. - Additionally, Agent notes starting from number 500 are placeholders, marked in the README as expected to be released later. Usage and notes (as stated in the README): - The notes are provided as Jupyter Notebooks. It is recommended to download them locally for viewing, as images or formulas may not display completely when rendered online on GitHub. - It is recommended to open them with Anaconda's Jupyter Notebook; images may not display properly when opened with PyCharm. - You can install the Jupyter Notebook directory plugin to quickly navigate between chapters. Other information: - The README provides a cloud drive link and extraction code for the accompanying datasets, and states that if the link fails, you can contact the author via WeChat. - The author has established a deep learning study group for self-learners to communicate. - The README also contains a lot of content unrelated to the notes, including career guidance, resume revision, internal referral company lists, paper tutoring, and other personal services, as well as many images and promotional text. These are the author's additional information and are not directly related to the technical content of the notes. Overall, the main value of this repository lies in centrally organizing and numbering study notes from multiple mainstream deep learning courses, making it convenient for Chinese readers to browse by topic and order, and suitable as a supplementary resource index for self-studying deep learning.