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    11 videos · 11 hr 18 min
    Playlists

    Learn deep learning from zero

    Curated by HackerTube · 11 videos · 11 hr 18 min

    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.

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    1. 1
      18:40

      But what is a neural network? | Deep learning chapter 1

      3Blue1Brown · 25M views · 9 years ago

      HN's default first stop, with 16 recommenders: neurons, layers and digit recognition as animation, no code yet.

    2. 2
      3:07

      Neural Networks Demystified [Part 1: Data and Architecture]

      Welch Labs · 839K views · 12 years ago

      The 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.

    3. 3
      2:25:52

      The spelled-out intro to neural networks and backpropagation: building micrograd

      Andrej Karpathy · 4.4M views · 4 years ago

      The step where backprop stops being magic, because you build a tiny autograd engine yourself, and HN's top-scored video in machine learning.

    4. 4
      2:35:53

      Tensorflow and deep learning - without a PhD by Martin Görner

      Devoxx · 610K views · 9 years ago

      Two practical hours on MNIST and convnets that cover what textbooks skip: why networks fail to train and how to fix them.

    5. 5
      28:48

      LSTM is dead. Long Live Transformers!

      Seattle Applied Deep Learning · 541K views · 6 years ago

      Leo Dirac covers bag of words, RNNs and LSTMs, then attention, so you know what problem transformers solved before you meet them.

    6. 6
      27:07

      Attention Is All You Need

      Yannic Kilcher · 814K views · 8 years ago

      Yannic Kilcher reads the original transformer paper with you, a good point to see the source before the polished explanations.

    7. 7
      27:14

      Transformers, the tech behind LLMs | Deep Learning Chapter 5

      3Blue1Brown · 12M views · 2 years ago

      The visual version of the paper, which many HN commenters say is where embeddings and attention finally clicked.

    8. 8
      1:56:20

      Let's build GPT: from scratch, in code, spelled out.

      Andrej Karpathy · 8.2M views · 3 years ago

      The capstone build: a GPT in PyTorch in two hours, which 14 HN commenters call the best video on the topic.

    9. 9
      24:07

      AI can't cross this line and we don't know why.

      Welch Labs · 2.5M views · 2 years ago

      After building one model, step back to scaling laws and the compute-efficient frontier that every model seems to run up against.

    10. 10
      39:43

      Do we need Attention? - Linear RNNs and State Space Models (SSMs) for NLP

      Sasha Rush · 35K views · 3 years ago

      Sasha Rush on the main alternative to attention, so you finish knowing where the architecture debate is heading.

    11. 11
      1:31:13

      A Hackers' Guide to Language Models

      Jeremy Howard · 580K views · 3 years ago

      The practical close: Jeremy Howard on using, prompting and fine-tuning the models you now understand from the inside.