![Neural Networks Demystified [Part 1: Data and Architecture]](https://i.ytimg.com/vi/bxe2T-V8XRs/hq2.jpg)
11 videos · 11 hr 18 minLearn deep learning from zero
A study plan for programmers who want to understand modern language models properly. It starts with visual intuition, moves to building backprop and a convnet by hand, covers why transformers replaced RNNs, and finishes with building and using GPT-style models. Each video assumes you have watched the ones before it. Plan for about 15 hours, and the from-scratch lectures are worth coding along with.
Play all on YouTube- 118:40
But what is a neural network? | Deep learning chapter 1
3Blue1Brown · Picked by 16 · 2017–2025HN's default first stop, with 16 recommenders: neurons, layers and digit recognition as animation, no code yet.
- 23:07
Neural Networks Demystified [Part 1: Data and Architecture]
Welch Labs · Picked by 3 · 2015–2018The first of Welch Labs' very short videos that turn the picture into a working Python network, which an HN commenter called one of the clearest walkthroughs online.
- 32:25:52
The spelled-out intro to neural networks and backpropagation: building micrograd
Andrej Karpathy · Picked by 17 · 2022–2025The step where backprop stops being magic, because you build a tiny autograd engine yourself, and HN's top-scored video in machine learning.
- 42:35:53
Tensorflow and deep learning - without a PhD by Martin Görner
Devoxx · Picked by 6 · 2017–2020Two practical hours on MNIST and convnets that cover what textbooks skip: why networks fail to train and how to fix them.
- 5
28:48LSTM is dead. Long Live Transformers!
Seattle Applied Deep Learning · Picked by 4 · 2020–2023Leo Dirac covers bag of words, RNNs and LSTMs, then attention, so you know what problem transformers solved before you meet them.
- 6
27:07Attention Is All You Need
Yannic Kilcher · Picked by 4 · 2020–2025Yannic Kilcher reads the original transformer paper with you, a good point to see the source before the polished explanations.
- 7
27:14Transformers, the tech behind LLMs | Deep Learning Chapter 5
3Blue1Brown · Picked by 13 · 2024–2026The visual version of the paper, which many HN commenters say is where embeddings and attention finally clicked.
- 8
1:56:20Let's build GPT: from scratch, in code, spelled out.
Andrej Karpathy · Picked by 14 · 2023–2025The capstone build: a GPT in PyTorch in two hours, which 14 HN commenters call the best video on the topic.
- 9
24:07AI can't cross this line and we don't know why.
Welch Labs · Picked by 2 · 2024–2025After building one model, step back to scaling laws and the compute-efficient frontier that every model seems to run up against.
- 10
39:43Do we need Attention? - Linear RNNs and State Space Models (SSMs) for NLP
Sasha Rush · Picked by 3 · 2024Sasha Rush on the main alternative to attention, so you finish knowing where the architecture debate is heading.
- 11
1:31:13A Hackers' Guide to Language Models
Jeremy Howard · Picked by 8 · 2023–2024The practical close: Jeremy Howard on using, prompting and fine-tuning the models you now understand from the inside.