alt Hacker NewsNeural Networks: Zero to Hero
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I like Karpathy, we come from the same lineage and I am very proud of him for what he's accomplished, he's a very impressive guy.
In regards to deep learning, building deep learning architecture is one of my greatest joys in finding insights from perceptual data. Right now, I'm working on spatiotemporal data modeling to build prediction systems for urban planning to improve public transportation systems. I build ML infrastructure too and plan to release an app that deploys the model in the wild within event streams of transit systems.
It took me a month to master the basics and I've spent a lot of time with online learning, with Deeplearning.ai and skills.google. Deeplearning.ai is ok, but I felt the concepts a bit dated. The ML path at skills.google is excellent and gives a practical understanding of ML infrastructure, optimization and how to work with gpus and tpus (15x faster than gpus).
But the best source of learning for me personally and makes me a confident practitioner is the book by Francois Chollet, the creator of Keras. His book, "Deep Learning with Python", really removed any ambiguity I've had about deep learning and AI in general. Francois is extremely generous in how he explains how deep learning works, over the backdrop of 70 years of deep learning research. Francois keeps it updated and the third revision was made in September 2025 - its available online for free if you don't want to pay for it. He gives you the recipe for building a GPT and Diffusion models, but starts from the ground floor basics of tensor operations and computation graphs. I would go through it again from start to finish, it is so well written and enjoyable to follow.
The most important lesson he discusses is that "Deep learning is more of an art than a science". To get something working takes a good amount of practice and the results on how things work can't always be explained.
He includes notebooks with detailed code examples with Tensorflow, Pytorch and Jax as back ends.
Deep learning is a great skill to have. After reading this book, I can recreate scientific abstracts and deploy the models into production systems. I am very grateful to have these skills and I encourage anyone with deep curiosity like me to go all in on deep learning.
I have lots of non-AI software experience but nothing with AI (apart from using LLMs like everyone else). Also I did an introductory university course in AI 20 years ago that I’ve completely forgotten.
Where do I get to if I go through this material?
Enough to build… what? Or contribute on… ? Enough knowledge to have useful conversations on …? Enough knowledge to understand where to … is useful and why?
Where are the limits, what is it that the AI researchers have that this wouldn’t give?
I'm not sure how it compares, but another option is the Hugging Face learning portal [0]. I'm doing the Deep RL Course and so far it's pretty straight forward (although when it gets math heavy I'm going to suffer).
A couple years ago I wrote a tutorial how to build a Neural Network in NumPy from scratch.¹
¹ https://matthodges.com/posts/2022-08-06-neural-network-from-...
This is a good resource, however for about 99.99% of people, you are most likely to just use a foundation model like ChatGPT, Claude, Gemini etc. so this knowledge/training will get you neither here or there. I would suggest you look into another Karpathy's video -- Deep Dive into LLMs like ChatGPT.
A bit of shameless plug, I wrote 2 articles about this after doing the course a while ago.
https://martincapodici.com/2023/07/15/no-local-gpu-no-proble...
https://martincapodici.com/2023/07/19/modal-com-and-nanogpt-...
I don't even have enough knowledge to grasp the first video. Is there a list of knowledge requirements to look at?
Has anyone gone through cs231n and this as well?
I went through the former and it was one of the best classes I’ve ever taken. But I’ve been procrastinating on going through this because it seems like there’s a lot of overlap and the benefit seems marginal (I guess transformers are covered here?).
Is there a text tutorial of this approach building NN from scratch? As a dad I simply don’t have a chance to watch this. Also maybe something for more math inclined? (MS in math) Deep learning in python that is recommended in other comments is way too basic and slow and hand wavy imo.
I saw this on a comment [0] and thought it deserved a post.
Highly recommend this as well. Does a great job of helping you build intuition for why things like gradient descent and normalization work. Also gets into the weeds on training dynamics and how to ensure they are behaving properly
"Prerequisites: ... intro-level math (e.g. derivative, gaussian)"
Anyone got recommendations for learning resources for this type of math? Realizing now that I might be a bit behind on my intro-level math.
This is great, but if I'm starting ML from scratch, what would you recommend? I'm coming from a webdev background and have used LLMs but nothing about ML, might even need the refresher on math, I think.
I wish Karpathy's star fleet academy becomes a huge success.
This new? Hasn't the zero-to-hero course been around for a while?
A bit of a tangential topic — what would you recommend to someone who wants to get into computer vision and 3D (NERFs, photogrammetry, 3DGS etc)?
For someone who has a middling amount of math knowledge, what would you recommend?
I went to uni 15y ago, but only had "proper" math in the first 2 semesters, let's says something akin to Calculus 1 and Linear Algebra 1. Hated math back then, plus I had horrible habits.
I just finished this series and found it very useful. Especially the back-propagation lectures.
its fun seeing HN articles with huge upvotes but no comments, similar to when some super esoteric maths gets posted: everyone upvotes out of a common understanding of its genius, but indeed by virtue of its genius most of us are not sufficiently cognitively gifted to provide any meaningful commentary.
the karpathy vids are very cool but having watched it, for me the takeaway was "i had better leave this for the clever guys". thankfully digital carpentry and plumbing is still in demand, for now!
what next now tho? i co-incidentally completed watching his last vid of training up gpt-2 today :-) .
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I’ve gone through this series of videos earlier this year.
In the past I’ve gone through many “educational resources” about deep neural networks - books, coursera courses (yeah, that one), a university class, the fastai course - but I don’t work with them at all in my day to day.
This series of videos was by far the best, most “intuition building”, highest signal-to-noise ratio, and least “annoying” content to get through. Could of course be that his way of teaching just clicks with me, but in general - very strong recommend. It’s the primary resource I now recommend when someone wants to get into lower level details of DNNs.