交大密院Deep Learning学习手册

UM-SJTU-JI Deep learning Hands-on Tutorial

Please find the hands-on tutorials of different sessions in corresponding markdown files. (e.g. Session_1.md)

Session 0: 在开始写代码之前，分享一些学习资源

Before you start to code, this session is about some recommended resources to start Deep Learning.

Table of Contents

• Books
• Online Courses
• Tutorials and Blogs
• Research Papers
• YouTube Channels
• Websites
• Podcasts

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Online Courses

1. MIT Introduction to Deep Learning （Strongly Recommended! If you only have time for only one tutorial, please watch this one）

   • A free course offered by MIT.
   • Link to Course

2. Deep Learning Specialization by Andrew Ng on Coursera

   • Covers the foundations and advanced topics in deep learning.
   • Link to Course

3. Fast.ai Courses

   • Practical deep learning courses for coders.
   • Link to Course

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Books

1. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville （Strongly Recommended!）

   • A comprehensive book that provides both theory and practical examples.
   • Link to Book

2. Python Machine Learning by Sebastian Raschka and Vahid Mirjalili

   • Great for beginners and covers various machine learning techniques along with deep learning.
   • Link to Book

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

   • A very practical guide to understanding machine learning with Python libraries.
   • Link to Book

4. Neural Networks and Deep Learning by Michael Nielsen

   • An online book that is available for free.
   • Link to Book

5. 李沐 动手学深度学习 （Strongly Recommended!）

   • Book + code + Chinese available.
   • Link to Book

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Tutorials and Blogs

1. Towards Data Science on Medium

   • Various articles, tutorials, and guides on machine learning and deep learning.
   • Link to Medium

2. Colah's Blog

   • Provides intuitive explanations for complex topics in deep learning.
   • Link to Blog

3. Distill.pub

   • Offers clear and interactive research publications.
   • Link to Website

4. Pytorch Official Turorial (Strongly Recommended!)

   • OFFICIAL hands-on tutorial by Pytorch, if you success with this tutorial, you can skip the session 1 and session 2 in UM SJTU JI tutorial.
   • Link to Website

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Research Papers

1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

   • Link to Paper

2. ResNet: Deep Residual Learning for Image Recognition

   • Link to Paper

3. Transformers: Attention Is All You Need

   • Link to Paper

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YouTube Channels

1. 3Blue1Brown

   • Excellent channel for visualizing complex topics.
   • Link to Channel

2. Two Minute Papers

   • Summarizes research papers in about two minutes.
   • Link to Channel

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Websites

1. arXiv.org

   • Open-access archive for scholarly articles.
   • Link to Website

2. Kaggle

   • Offers various datasets and competitions to practice your skills.
   • Link to Website

3. Grand-challenge

   • Offers various datasets and competitions for medical AI.
   • Link to Website

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Podcasts

1. Data Skeptic

   • Covers topics in data science and machine learning.
   • Link to Podcast

2. The AI Podcast by Lex Fridman

   • Deep, thoughtful interviews with leaders in the field of AI.
   • Link to Podcast

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This is by no means an exhaustive list but should serve as a good starting point for diving into the field of deep learning.

© This github repo is maintained by Yutong Ban 班雨桐 @JI, Chongye Yang 杨崇烨 @JI, Kunyi Yang 杨坤燚 @JI, Junjie Liu 刘俊杰 @ JI.