Python, Deep Learning, and LLMs

What it does

This book is a coding and machine learning bootcamp in textbook form. You will get hands-on experience with the Python programming language, essential math concepts, neural nets – and, by the end, you will have coded and trained a pocket-sized language model. A high school math background is all you need to get started; no prior programming experience is required.

A Crash Course for Complete Beginners

This book is a coding and machine learning bootcamp in textbook form. You will get hands-on experience with the Python programming language, essential math concepts, neural nets – and, by the end, you will have coded and trained a pocket-sized language model. A high school math background is all you need to get started; no prior programming experience is required. Put in the work, and soon you will know, in concrete detail, how to create a miniature mind inside a chunk of silicon using code. If that is not magic, what is? @misc{tkachenko2025python2llms, author={Tkachenko, Yegor}, title={Python, Deep Learning, and {LLMs}: A Crash Course for Complete Beginners}, year={2025},…from python2llms.org

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  I built a tiny LLM to demystify how language models workApr 2026 · github.com · ▲915

  Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.

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  I co-wrote a book on training Deep Learning models in Go2019 · ▲45

  A friend and I wrote a book on how to build and train Deep Learning models in Go. We wanted it to be a useful reference for deep learning basics for Go programmers. Deep Learning is slowly seeping into everything we use every day and we thought it would be great if more people could do it in Go. The book is available here and on Amazon as well. https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-go We would appreciate any feedback and we're always looking to improve.

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  Natural Language Processing Demystified (Part One)2022 · nlpdemystified.org · ▲166

  Hi HN: I published part one of my free NLP course. The course is intended to help anyone who knows Python and a bit of math go from the very basics all the way to today's mainstream models and frameworks. I strive to balance theory and practice and so every module consists of detailed explanations and slides along with a Colab notebook (in most modules) putting the theory into practice. In part one, we cover text preprocessing, how to turn text into numbers, and multiple ways to classify and search text using "classical" approaches. And along the way, we'll pick up useful bits on how to use…

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  Python Machine Learning2017 · amazon.com · ▲80

  Learning machine learning has never been easier

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  Learn LLMs LeetCode Style2025 · github.com · ▲181

  LeetCode for PyTorch — 65 ML/AI interview problems from real interviews at Google, Meta, Anthropic. Jupyter notebooks, an auto-grader, and an MCP AI tutor. - Exorust/TorchLeet

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  I wrote a minimal deep learning library from scratch in Python2021 · github.com · ▲25

  A minimalist deep learning library written from scratch in Python - sradc/SmallPebble

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  Germany's new sovereign AI model Kolibri▲424

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  Notion + Loom, for video collaboration.

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  Strange Attractors▲804

  I went down the rabbit hole on a side project and ended up building this: Strange Attractors(https://blog.shashanktomar.com/posts/strange-attractors). It’s built with three.js. Working on it reminded me of the little "maths for fun" exercises I used to do while learning programming in early days. Just trying things out, getting fascinated and geeky, and being surprised by the results. I spent way too much time on this, but it was extreme fun. My favorite part: someone pointed me to the Simone Attractor on Threads. It is a 2D attractor and I asked GPT to extrapolate it to…

  AI · Oct 2025 · blog.shashanktomar.com

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  Autism Simulator▲779

  Hey all, I built this. It’s not trying to capture every autistic experience (that’d be impossible). It’s based on my own lived experience as well as that of friends on the spectrum. I'm trying to give people a feel for what masking, decision fatigue, and burnout can look like day-to-day. That’s hard to explain in words, but easier to show through choices and stats. I'm not trying to "define autism". I’ve gotten good feedback here about resilience, meds, and difficulty tuning. I’ll keep tweaking it. If even a few people walk away thinking, "ah, maybe that’s why my coworker struggles in those…

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  Director▲768

  Lovable for web automation

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