This is a comprehensive list of machine learning and deep learning resources. Resources cover the theory and implementation of LLM and image diffusion models. If you'd like to contribute a resource then please open a new issue or submit a PR.
This is a very new list of ML resources so the structure may change around while we figure out the best way to organize the resources. By all means let me know what you think and how it can be improved!
As you may have noticed most (or almost all!) resources focus on diffusion models. This is because where most of my focus has been these last few months. But I do want to transition to cover LLMs too. So if you have some good language based resources you'd like to share please let me know.
- Deep Learning & Machine Learning Resources
- arXiv - Main site
A collection of some of the seminal papers on machine learning, and deep learning.
- A Survey on Generative Diffusion Model [PDF]
- Attention Is All You Need [PDF]
- Auto-Encoding Variational Bayes [PDF]
- Classifier-Free Diffusion Guidance [PDF]
- Deep Unsupervised Learning using Nonequilibrium Thermodynamics [PDF]
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification [PDF]
- Denoising Diffusion Probabilistic Models [PDF]
- Denoising Diffusion Implicit Models [PDF]
- Improved Denoising Diffusion Probabilistic Models [PDF]
- Lecture Notes in Probabilistic Diffusion Models [PDF] - Some nice novel images and discussions of diffusion processes
- State of the Art on Diffusion Models for Visual Computing [PDF]
- Stable Diffusion 3 - Scaling Rectified Flow Transformers for High-Resolution Image Synthesis [PDF]
- The Matrix Calculus You Need For Deep Learning [PDF]
- Understanding Diffusion Models: A Unified Perspective [PDF]
- Variational Diffusion Models [PDF]
Some noteworthy YouTube channels, some of which have sub-links to relevant ML playlists and individual videos. Note: If a sub-link ends with (V) then it is an individual video rather than a playlist.
- AI Coffee Break with Letitia
- Artem Kirsanov
- The Key Equation Behind Probability - Entropy, Cross-entropy, KL divergence
- The Grandfather Of Generative Models - Boltzmann Machines
- 3Blue1Brown
- Neural Networks - See the two transformer videos in particular
- Andrej Karpathy
- CUDA Mode
- Computerphile
- DataScienceCastnet - Johno Whitaker
- DeepBean
- ExplainingAI
- Gabriel Mongaras - Some nice research paper walkthroughs.
- Hamel Husain
- Imperial College London
- Jeremy Howard
- Jia-Bin Huang
- Kapil Sachdeva
- Variational Inference & AutoEncoder - Concise and detailed.
- KL Divergence - CLEARLY EXPLAINED!
- Machine Learning at Berkeley
- Machine Learning & Simulation
- MIT HAN Lab
- MIT OpenCourseWare
- MLT Artificial Intelligence
- Outlier
- Ox educ
- Ritvikmath
- Sam Witteveen
- Stanford Online
- CS109 Introduction to Probability for Computer Scientists
- Stanford CS224N: NLP with Deep Learning - Stanford course on transformers with review sessions on Python, PyTorch, and Hugging Face.
- Stanford CS236: Deep Generative Models I 2023 I Stefano Ermon
- StatQuest - Highly recommended for probability and statistics.
- Tanishq Abraham
- Diffusion Models Study Group - Study group discussing many popular ML papers.
- Tübingen Machine Learning
- TWIML Community
- Umar Jamil
- Volodymyr Kuleshov (Cornell Tech)
- Weights & Biases
- W&B Fastbook Reading Group (Covers part1 of the fastai course)
- Yannic Kilcher
Collection of one-off individual videos.
- Napkin Math For Fine Tuning - Video from Johno on fine tuning.
- CVPR #18546 - Denoising Diffusion Models: A Generative Learning Big Bang
- Diffusion and Score-Based Generative Models (Yang Song)
- Evidence Lower Bound (ELBO) - Clearly Explained!
- Stable Diffusion Explained w/ Sai Kumar
- Kullback–Leibler divergence (KL divergence) intuitions - Intuitive breakdown of the KL divergence equation.
- MIT 6.S191: Deep Generative Modeling
- The Reparameterization Trick
- Class Central - Find courses from top Universities and companies.
- Fast.ai
- Hugging Face Diffusion Models Course - Comprehensive course on diffusion models from Hugging Face.
- Introduction to Deep Learning (MIT) - MIT course on deep learning, including a video on deep generative modeling.
- Machine Learning University
- MLOps Zoomcamp
- Reddit Thread on Advanced Courses - Discussion on advanced machine learning courses.
- Stable Diffusion Implementation - Implementation of Stable Diffusion from scratch.
There are many excellent textbooks available for machine learning and deep learning. Here are a few that I've come acrsoos that are particularly useful. Some of these have free online versions available in PDF or HTML format, and links to these where available are added after the book title.
- Deep Learning (Goodfellow, Bengio, Courville) [HTML]
- Deep Learning, A Visual Approach (Glassner)
- Dive into Deep Learning [HTML] - Interactive deep learning textbook with code, math, and discussions.
- Machine Learning Engineering Open Book - This is the GitHub repo for the book. A PDF is also available.
- Mathematics for Machine Learning [PDF]
- Neural Networks and Deep Learning - Free online book on neural networks and deep learning.
- Probabilistic Machine Learning (set of three books)
- Probability and Statistics - The Science of Uncertainty 2e [PDF] [Solutions manual]
- Python for Data Analysis, 3E - Free online version available
- Rectified Flow
- Understanding Deep Learning [PDF]
A collection of blogs and posts that post about various ML topics. Sub-links are individually selected posts from the related (parent) blog.
- Berkeley Artificial Intelligence Research
- Cambridge MLG
- Chris Levy
- Google DeepMind
- Distill
- Hugging Face
- Isamu Isozaki
- Jack Tol
- Jake Tae
- Jay Alammar
- Illustrated Transformer - Visual guide to understanding transformer architectures.
- The Illustrated Stable Diffusion
- Jeremy Jordan
- Variational Autoencoders Explained - Detailed explanation of VAEs with intuitive breakdowns.
- Kapil Sachdeva
- Lilian Weng
- Matthew N. Bernstein - Some gems here on various diffusion related topics
- MIT News - AI
- OpenAI
- Paperspace
- Probably Overthinking It - Some really nice posts on general probability topics.
- Radek Osmulski
- Salman Naqvi
- Sander Dieleman - Collection of posts on various advanced ML topics.
- Perspectives on diffusion - Insights on diffusion models.
- Diffusion models are autoencoders
- Geometry of Diffusion Models - Exploration of the geometric aspects of diffusion models.
- Scratchapixel - Welcome to Computer Graphics (general computer graphics programming)
- Stable Diffusion Art
- Stability.ai
- The Latent: Code the Maths - 3 blog posts on DDPMs
- Vishal Bakshi
Some interesting and useful individual blog posts.
- Classification loss function as comparing 2 vectors - Intuition of cross-entropy viewed as comparing 2 vectors.
- Diffusion Models - Wiki
- Diffusion Models From Scratch - In-depth tutorial covering derivations and core concepts of diffusion models.
- Generative Modeling by Estimating Gradients of the Data Distribution - Detailed blog post on score-based generative models and their advantages.
- Introduction to Attention Mechanism
- Understanding PyTorch with an example: a step-by-step tutorial
- The ELBO in Variational Inference
- Step by Step visual introduction to Diffusion Models
- What is a variational autoencoder?
Some tutorials and other resources located on GitHub.
This is a collection of GitHub repositories of students of the Fasti.ai course (parts 1 & 2).
- math-fastai - This repository includes some really nice probability notebooks.
- Fastai
- Hugging Face
- Stable Diffusion
- Answer.ai
- Eureka Labs
- Fast.ai
- Hugging Face
- Diffusers tutorials
- General documentation
- Hugging Face Spaces - Host and demo models
- nbdev
- PyTorch
- Claudette (Answer.ai)
- Jupyter
- Zotero
- Gradio
- Streamlit
- Cursor
- LightningAI
- Kaggle
- Fal
Various services for hosting and deploying deep learning models.
- Replicate
- Paperspace
- LightningAI
- Paperspace
- Lambda
- Jarvislabs
- Vast.ai
- OctoAI
- Anthropic
- Answer.AI
- Qwak - Now part of JFrog
Machine learning on Twitter is very active. Here are some of the people you should be following!
- Tanishq Abraham - @iScienceLuvr
- Jeremy Howard - @jeremyphoward
- Andrej Karpathy - @karpathy
- Jonathan Whitaker - @johnowhitaker
- Prof. Chris Bishop's NEW Deep Learning Textbook!
- This is why Deep Learning is really weird (Prof. Simon Prince)
- Awesome Generative AI Guide - Curated list of generative AI resources, courses, and materials.
- Experiments with Google (deprecated as of 2022) - Showcasing various AI experiments.
- Hacker News Showcase - People showcasing their projects.
- Labs.Google - Experiment with the future of AI.
- ML Ops Guide (Chip Huyen) - Collection of resources for machine learning operations.
- Multi-AI Agent Systems with CrewAI - Course on building multi-agent AI systems.
- Papers With Code
- Product Hunt - Inspiration for some AI products others have launched.
- Roboflow object detection in sports
- Find Trending Papers
- UNet Diffusion Model in CUDA - Implementation of a UNet diffusion model in pure CUDA.
- VAE Slides - Lecture slides on Variational Autoencoders.
- The Bright Side of Mathematics
- MLOps guide - Chip Huyen
- ghapi - GitHub API from fast.ai