The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

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Slightly off topic: I'm interested in taking part in the Vesuvius challenge[0], but I don't have a background in ML, just a regular web developer. Does anyone have suggestions on how to get started? I planned to get some background on practical ML by working through Karpathy's Zero to Hero series along with the Understanding Deep Learning book. Would that be enough or anything else I should learn? I plan to understand the existing solutions to last year's prize and then pick a smaller sub challenge.

[0] https://scrollprize.org/

> In contrast, the AI technology of the current golden age, which began in the mid-2010s, has consistently exceeded our expectations.

Well, until recently, that is. It looks like we hit the wall as for what LLMs can do - some might call it a plateau of productivity. Namely, as far as coding is concerned, LLMs can successfully create chunk of code of limited length and tiny programs, can also review small pieces of code and suggest improvements that are not related to the context of the whole program (unless it can fit in the context window). In spite of huge effort put in creating a system where LLM agents could work together to create software such as AutoGPT, no non-trivial program has been created in this way so far.

These slides from Lucas Beyer are pretty nice. https://docs.google.com/presentation/d/1ZXFIhYczos679r70Yu8v...

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.

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