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
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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)
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)
