Understanding Machine Learning

Self Study Summer Projects

Source Material

Based on the video by Aleph 0 on ML self-study

M1 MIAGE IDA course work

Cheatsheet

http://ml-cheatsheet.readthedocs.io/

Book Recommendations

• Grokking Deep Learning
• The 100-page Machine Learning Handbook by Andriy Burkov
• Deep Learning with PyTorch by Laura Mitchell, Sri Yogesh K, and Vishnu Subramanian

Topics and Resources

French Alternative: AiForYou

1a. Feed-Forward Neural Networks

• Chapter 1 of Book by Michael Nielsen: Neural Networks and Deep Learning
• Grokking Deep Learning (Chapters 2, 3)
• The 100-page Machine Learning Handbook (Chapters 3.1, 3.2, Chapter 6)

1b. Gradient Descent / Backpropagation

• Grokking Deep Learning (Chapters 4, 6)
• Chapter 2 of Book by Michael Nielsen: Neural Networks and Deep Learning

2. Convolutional Neural Networks

• Videos by Computerphile:
  • Blurs and Filters
  • Edge Detection (Sobel Operator)
  • Intro to CNNs
  • Any new videos by Computerphile
• Deep Learning with PyTorch (Chapter 5)

3. Recurrent Neural Networks

• Grokking Deep Learning (Chapters 11, 12)
• Video by Serrano Academy: A friendly introduction to Recurrent Neural Networks
• Stat Quest: Recurrent Neural Networks (RNNs), Clearly Explained!!!

4. Autoencoders

• Deep Learning with PyTorch (Chapter 6)
• Video playlist by Digital Sreeni: Autoencoders and their applications

5. Reinforcement Learning

• Deep Learning with PyTorch (Chapter 9)

6. Attention

• Blog post by Jay Alammar: The Illustrated Transformer
• Lecture by Stanford Online: Introduction to Transformers

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