---
title: "Deep learning resources"
description: "Curated deep learning courses, lectures, and books — the shortest path from neural network basics to modern architectures."
canonical_url: "https://fanout.sh/ai/deep-learning"
md_url: "https://fanout.sh/ai/deep-learning.md"
access: "public"
---

# Deep learning resources

Curated deep learning courses, lectures, and books — the shortest path from neural network basics to modern architectures.

## Public deep learning resources

From neural network fundamentals to transformers and LLMs — books, courses, explainers, and hands-on tutorials across every core deep learning topic.

## Start here

### [Deep Learning Roadmap](https://aigents.co/learn/roadmaps/deep-learning-roadmap)

- Type: tutorial

### [A Short Chronology Of Deep Learning For Tabular Data](https://sebastianraschka.com/blog/2022/deep-learning-for-tabular-data.html)

- Author: Sebastian Raschka
- Type: explainer

### [Understanding AI](https://leerob.com/n/ai)

- Author: Lee Robinson
- Type: explainer

### [A Neural Network Playground](https://playground.tensorflow.org/)

- Author: TensorFlow
- Type: tool

### [An Opinionated Guide to ML Research](http://joschu.net/blog/opinionated-guide-ml-research.html)

- Author: John Schulman
- Type: tutorial

## Books

### [Deep Learning](https://www.deeplearningbook.org/)

- Author: Ian Goodfellow
- Type: book

### [Understanding Deep Learning](https://udlbook.github.io/udlbook/)

- Type: book

### [Dive into Deep Learning](https://d2l.ai/)

- Type: book

### [The Little Book of Deep Learning](https://fleuret.org/public/lbdl.pdf)

- Author: François Fleuret
- Type: book

### [Grokking Deep Learning](https://cdn.ttgtmedia.com/rms/pdf/grokking_deep_learning.pdf)

- Type: book

## Courses

### [DeepLearning.AI](https://deeplearning.ai/)

- Author: Andrew Ng
- Type: course

### [NYU Deep Learning](https://atcold.github.io/NYU-DLSP21/)

- Author: Yann LeCun & Alfredo Canziani
- Type: course

### [The Complete Mathematics of Neural Networks and Deep Learning](https://www.youtube.com/watch?v=Ixl3nykKG9M)

- Type: video

### [Intro to Deep Learning](https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51)

- Author: Sebastian Raschka
- Type: course

### [Practical Deep Learning for Coders](https://course.fast.ai/)

- Author: fast.ai
- Type: course

## CNN

### [CNN from Scratch with pure Mathematical Intuition](https://lunar-joke-35b.notion.site/CNN-from-Scratch-with-pure-Mathematical-Intuition-a201ef0ca1314058a1707a3ae260981e)

- Type: tutorial

### [Convolutional Neural Network (CNN): A Complete Guide](https://learnopencv.com/understanding-convolutional-neural-networks-cnn/)

- Type: tutorial

### [CNN Explainer](https://poloclub.github.io/cnn-explainer/)

- Author: Polo Club
- Type: explainer

### [ConvNetJS — Deep Learning in your browser](https://cs.stanford.edu/people/karpathy/convnetjs/index.html)

- Author: Karpathy
- Type: tool

### [Convolutional Neural Networks Explained (CNN Visualized)](https://www.youtube.com/watch?v=pj9-rr1wDhM)

- Type: video

## RNN

### [Recurrent Neural Networks Tutorial, Part 1 — Introduction to RNNs](https://dennybritz.com/posts/wildml/recurrent-neural-networks-tutorial-part-1/)

- Author: Denny Britz
- Type: tutorial

### [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)

- Author: Christopher Olah
- Type: explainer

### [Predict Stock Prices Using RNN: Part 1](https://lilianweng.github.io/posts/2017-07-08-stock-rnn-part-1/)

- Author: Lilian Weng
- Type: tutorial

### [Recurrent Neural Networks (RNN)](https://madewithml.com/courses/foundations/recurrent-neural-networks/)

- Author: Made With ML
- Type: tutorial

### [RNNs and LSTMs](https://web.stanford.edu/~jurafsky/slp3/8.pdf)

- Author: Jurafsky, Stanford
- Type: explainer

## LLM

### [Umar Jamil (YouTube)](https://www.youtube.com/@umarjamilai)

- Author: Umar Jamil
- Type: video

### [Build a Large Language Model (From Scratch)](https://livebook.manning.com/book/build-a-large-language-model-from-scratch/title/)

- Author: Sebastian Raschka
- Type: book

### [Create a Large Language Model from Scratch with Python](https://www.youtube.com/watch?v=UU1WVnMk4E8&t=4303s)

- Author: elliotarledge
- Type: video

### [Intro to Transformers (slides)](https://docs.google.com/presentation/d/1ZXFIhYczos679r70Yu8vV9uO6B1J0ztzeDxbnBxD1S0/edit#slide=id.ge2832e38b9_0_21)

- Author: giffmana
- Type: explainer

### [\[M2L 2024\] Transformers](https://www.youtube.com/watch?v=bMXqnLiVgLk)

- Author: Lucas Beyer (giffmana)
- Type: video

## PyTorch

### [Zero to Mastery Learn PyTorch for Deep Learning](https://www.learnpytorch.io/)

- Author: Daniel Bourke
- Type: course

### [Learn PyTorch for deep learning in a day. Literally.](https://www.youtube.com/watch?v=Z_ikDlimN6A)

- Author: Daniel Bourke
- Type: video

### [PyTorch internals](http://blog.ezyang.com/2019/05/pytorch-internals/)

- Author: ezyang
- Type: tutorial

### [MiniTorch](https://minitorch.github.io/)

- Type: tool

### [PyTorch is dead. Long live JAX.](https://neel04.github.io/my-website/blog/pytorch_rant/)

- Type: tutorial

## Karpathy

### [Blog](https://karpathy.github.io/)

- Author: Andrej Karpathy
- Type: tutorial

### [Neural Networks: Zero to Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)

- Author: Andrej Karpathy
- Type: course

### [CS231n Winter 2016](https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)

- Author: Andrej Karpathy
- Type: course

### [CS231n Winter 2016 — Course Site](https://cs231n.stanford.edu/2016/)

- Author: Stanford
- Type: course

### [Eureka Labs AI](https://github.com/EurekaLabsAI)

- Author: Andrej Karpathy
- Type: repo

## 3Blue1Brown

### [Neural Networks (Articles)](https://www.3blue1brown.com/topics/neural-networks)

- Author: 3Blue1Brown
- Type: explainer

### [Neural Networks (Playlist)](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)

- Author: 3Blue1Brown
- Type: video

### [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/)

- Author: Suggested by 3B1B
- Type: book

### [Calculus on Computational Graphs: Backpropagation](https://colah.github.io/posts/2015-08-Backprop/)

- Author: Christopher Olah (suggested by 3B1B)
- Type: explainer

### [Neural Networks Demystified](https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU)

- Author: Suggested by 3B1B
- Type: video

## Build from Scratch

### [zero_to_gpt — Go from no DL knowledge to implementing GPT](https://github.com/VikParuchuri/zero_to_gpt)

- Author: Vik Paruchuri
- Type: repo

### [llama3-from-scratch — One matrix multiplication at a time](https://github.com/naklecha/llama3-from-scratch)

- Author: naklecha
- Type: repo

### [fromthetensor — From the Tensor to Stable Diffusion (9-week course)](https://github.com/jla524/fromthetensor)

- Type: repo

### [Building autograd engine (tinytorch)](https://pythonstuff.com/blog/buinging%20own%20autograd%20engine%20tinytorch%2001)

- Type: tutorial

### [Autodidax: JAX core from scratch](https://jax.readthedocs.io/en/latest/autodidax.html)

- Author: JAX
- Type: tutorial

## Diffusion Models

### [How diffusion models work: the math from scratch](https://theaisummer.com/diffusion-models/)

- Author: AI Summer
- Type: tutorial

### [Understanding Diffusion Models: A Unified Perspective](https://calvinyluo.com/2022/08/26/diffusion-tutorial.html)

- Author: Calvin Luo
- Type: tutorial

### [Step-by-Step Diffusion: An Elementary Tutorial](https://arxiv.org/pdf/2406.08929)

- Type: paper

### [Generative score-based diffusion (Equinox example)](https://docs.kidger.site/equinox/examples/score_based_diffusion/)

- Type: tutorial

### [Diffusion Models — bit by bit](https://lunar-joke-35b.notion.site/Diffusion-Models-bit-by-bit-10fba4b6a3fa80458d16e58036875747)

- Type: tutorial

## Interpretability

### [Concrete Steps to Get Started in Transformer Mechanistic Interpretability](https://www.neelnanda.io/mechanistic-interpretability/getting-started)

- Author: Neel Nanda
- Type: tutorial

### [OpenAI Microscope — Neural network visualization](https://microscope.openai.com/models)

- Author: OpenAI
- Type: tool

### [Why Neural Networks can learn (almost) anything](https://www.youtube.com/watch?v=0QczhVg5HaI)

- Type: video

### [Watching Neural Networks Learn](https://www.youtube.com/watch?v=TkwXa7Cvfr8)

- Type: video

### [What Are Neural Networks Even Doing? (Manifold Hypothesis)](https://www.youtube.com/watch?v=pdNYw6qwuNc)

- Type: video

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