I made a list of all the free resources I used to study ML and deep learning to become an ML engineer at FAANG, so I think it'll be helpful to follow these resources: https://www.trybackprop.com/blog/top_ml_learning_resources (links in the blog post)
Fundamentals
Linear Algebra – 3Blue1Brown's Essence of Linear Algebra series, binged all these videos on a one hour train ride visiting my parents
Multivariable Calculus – Khan Academy's Multivariable Calculus lessons were a great refresher of what I had learned in college. Looking back, I just needed to have reviewed Unit 1 – intro and Unit 2 – derivatives.
Calculus for ML – this amazing animated video explains calculus and backpropagation
Information Theory – easy-to-understand book on information theory called Information Theory: A Tutorial Introduction.
Statistics and Probability – the StatQuest YouTube channel
Machine Learning
Stanford Intro to Machine Learning by Andrew Ng – Stanford's CS229, the intro to machine learning course, published their lectures on YouTube for free. I watched lectures 1, 2, 3, 4, 8, 9, 11, 12, and 13, and I skipped the rest since I was eager to move onto deep learning. The course also offers a free set of course notes, which are very well written.
Caltech Machine Learning – Caltech's machine learning lectures on YouTube, less mathematical and more intuition based
Deep Learning
Andrej Karpathy's Zero to Hero Series – Andrej Karpathy, an AI researcher who graduated with a Stanford PhD and led Tesla AI for several years, released an amazing series of hands on lectures on YouTube. highly highly recommend
Neural networks – Stanford's CS231n course notes and lecture videos were my gateway drug, so to speak, into the world of deep learning.
Transformers and LLMs
Transformers – watched these two lectures: lecture from the University of Waterloo and lecture from the University of Michigan. I have also heard good things about Jay Alammar's The Illustrated Transformer guide
ChatGPT Explainer – Wolfram's YouTube explainer video on ChatGPT
Interactive LLM Visualization – This LLM visualization that you can play with in your browser is hands down the best interactive experience with an LLM.
Financial Times' Transformer Explainer – The Financial Times released a lovely interactive article that explains the transformer very well.
Efficient ML and GPUs
How are Microchips Made? – This YouTube video by Branch Education is one of the best free educational videos on the internet, regardless of subject, but also, it's the best video on understanding microchips.
CUDA – My FAANG coworkers acquired their CUDA knowledge from this series of lectures.
TinyML and Efficient Deep Learning Computing – 2023 lectures on efficient ML techniques online.
Chip War – Chip War is a bestselling book published in 2022 about microchip technology whose beginning chapters on the invention of the microchip actually explain CPUs very well
Fundamentals Linear Algebra – 3Blue1Brown's Essence of Linear Algebra series, binged all these videos on a one hour train ride visiting my parents
Multivariable Calculus – Khan Academy's Multivariable Calculus lessons were a great refresher of what I had learned in college. Looking back, I just needed to have reviewed Unit 1 – intro and Unit 2 – derivatives.
Calculus for ML – this amazing animated video explains calculus and backpropagation
Information Theory – easy-to-understand book on information theory called Information Theory: A Tutorial Introduction.
Statistics and Probability – the StatQuest YouTube channel
Machine Learning Stanford Intro to Machine Learning by Andrew Ng – Stanford's CS229, the intro to machine learning course, published their lectures on YouTube for free. I watched lectures 1, 2, 3, 4, 8, 9, 11, 12, and 13, and I skipped the rest since I was eager to move onto deep learning. The course also offers a free set of course notes, which are very well written.
Caltech Machine Learning – Caltech's machine learning lectures on YouTube, less mathematical and more intuition based
Deep Learning Andrej Karpathy's Zero to Hero Series – Andrej Karpathy, an AI researcher who graduated with a Stanford PhD and led Tesla AI for several years, released an amazing series of hands on lectures on YouTube. highly highly recommend
Neural networks – Stanford's CS231n course notes and lecture videos were my gateway drug, so to speak, into the world of deep learning.
Transformers and LLMs Transformers – watched these two lectures: lecture from the University of Waterloo and lecture from the University of Michigan. I have also heard good things about Jay Alammar's The Illustrated Transformer guide
ChatGPT Explainer – Wolfram's YouTube explainer video on ChatGPT
Interactive LLM Visualization – This LLM visualization that you can play with in your browser is hands down the best interactive experience with an LLM.
Financial Times' Transformer Explainer – The Financial Times released a lovely interactive article that explains the transformer very well.
Residual Learning – 2023 Future Science Prize Laureates Lecture on residual learning.
Efficient ML and GPUs How are Microchips Made? – This YouTube video by Branch Education is one of the best free educational videos on the internet, regardless of subject, but also, it's the best video on understanding microchips.
CUDA – My FAANG coworkers acquired their CUDA knowledge from this series of lectures.
TinyML and Efficient Deep Learning Computing – 2023 lectures on efficient ML techniques online.
Chip War – Chip War is a bestselling book published in 2022 about microchip technology whose beginning chapters on the invention of the microchip actually explain CPUs very well