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Currently Playing: How to learn machine learning as a complete beginner: a self-study guide

A step-by-step roadmap of how to learn machine learning as a beginner. If you'd like to sign up for the Aleph 0 math / machine learning newsletter, fill out the form here: https://forms.gle/Rt1f5StAj3yZtakE6 ------------ BOOK RECOMMENDATIONS Grokking Deep Learning by Andrew Trask The 100-page Machine Learning Handbook by Andriy Burkov Deep Learning with PyTorch by Laura Mitchell, Sri Yogesh K, and Vishnu Subramanian 1a. FEED-FORWARD NEURAL NETWORKS Chapter 1 of Book by Michael Nielsen: https://neuralnetworksanddeeplearning.com/chap1.html Grokking Deep Learning (Chapters 2,3) The 100-page Machine Learning Handbook (Chapter 3.1, 3.2, Chapter 6) 1b. GRADIENT DESCENT / BACKPROPAGATION Grokking Deep Learning (Chapters 4,6) Chapter 2 of Book by Michael Nielsen: https://neuralnetworksanddeeplearning.com/chap2.html 2. CONVOLUTIONAL NEURAL NETWORKS Two videos by Computerphile: Blurs and filters: https://www.youtube.com/watch?v=C_zFhWdM4ic&ab_channel=Computerphile Edge detection: https://www.youtube.com/watch?v=uihBwtPIBxM&ab_channel=Computerphile Intro to CNNs: https://www.youtube.com/watch?v=py5byOOHZM8&t=240s&ab_channel=Computerphile Deep Learning with PyTorch (Chapter 5) 3. RECURRENT NEURAL NETWORKS Grokking Deep Learning (Chapters 11 and 12) Video by Serrano Academy: https://www.youtube.com/watch?v=UNmqTiOnRfg&ab_channel=Serrano.Academy Stat Quest: https://www.youtube.com/watch?app=desktop&v=AsNTP8Kwu80&ab_channel=StatQuestwithJoshStarmer 4. AUTOENCODERS Deep Learning with Pytorch (Chapter 6). Video playlist by Digital Sreeni: https://www.youtube.com/playlist?list=PLZsOBAyNTZwb-uK_a6ywrU3t0hy80G5QP 5. REINFORCEMENT LEARNING Deep Learning with Pytorch (Chapter 9). 6. ATTENTION Blog post by Jay Alammar: https://jalammar.github.io/illustrated-transformer/ Lecture by Stanford Online: https://www.youtube.com/watch?v=XfpMkf4rD6E&t=252s&ab_channel=StanfordOnline Intro: (0:00) Three book recommendations: (0:53) Feed-Forward Neural Networks: (2:06) Convolutional Neural Networks: (4:12) Recurrent Neural Networks: (5:21) Autoencoders: (6:36) Reinforcement Learning: (7:20) Attention: (7:54) General Tips: (9:06)