Machine Learning Resources

Research Papers
Ilya 30u30 - keshavchan
The Annotated Transformer
The First Law of Complexodynamics
The Unreasonable Effectiveness of RNNs
Understanding LSTM Networks
Recurrent Neural Network Regularization
Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
Pointer Networks
ImageNet Classification with Deep CNNs
Order Matters: Sequence to sequence for sets
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Deep Residual Learning for Image Recognition
Multi-Scale Context Aggregation by Dilated Convolutions
Neural Quantum Chemistry
Attention Is All You Need
Neural Machine Translation by Jointly Learning to Align and Translate
Identity Mappings in Deep Residual Networks
A Simple NN Module for Relational Reasoning
Variational Lossy Autoencoder
Relational RNNs
Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
Neural Turing Machines
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Scaling Laws for Neural LMs
A Tutorial Introduction to the Minimum Description Length Principle
Machine Super Intelligence
Kolmogorov Complexity and Algorithmic Randomness
CS231n Convolutional Neural Networks for Visual Recognition
Papers in 100 Lines of Code
A Mathematical Framework for Transformer Circuits
Attention Is All You Need
Neural Networks and Neural Language Models
A Neural Probabilistic Language Model
The NumPy array: a structure for efficient numerical computation
Random Forests
Statistical Modeling: The Two Cultures
The Llama 3 Herd of Models
Gradient-based learning applied to document recognition
A Symbolic Analysis of Relay and Switching Circuits
A Logical Calculus of the Ideas Immanent in Nervous Activity
Intelligent Machinery
Long Short-term Memory
VISUALIZING AND UNDERSTANDING RECURRENT NETWORKS
Learning representations by back-propagating errors
Programming a Computer for Playing Chess
Perceptron
A Mathematical Theory of Communication
Neural Machine Translation by Jointly Learning to Align and Translate
Effective Approaches to Attention-based Neural Machine Translation
Sequence to Sequence Learning with Neural Networks
A Tutorial on Principal Component Analysis
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Self-Driving Cars: A Survey
Generating Sequences With Recurrent Neural Networks
Memory Networks
MemoryBank: Enhancing Large Language Models with Long-Term Memory
An overview of gradient descent optimization algorithms
Blogs
rsrch.space
Andrej Karpathy
Lilian Weng
Christopher Olah - colah
Chip Huyen
Eugene Yan- eugeneyan
Sebastian Raschka
himanshu
cneuralnetwork
siboehm
Mat Miller
Alfredo Canziani
sumit.ml
maharshi
naklecha
Greg Brockman
Michael Nielsen
Simon Willison
Hamel
saurabh
paneer's blog
Florian Hartmann
Leonardo Araujo Santos
Alexandru Burlacu
Victor Zhou
Yash Shah
Denny Britz
bair blog
anthropic blog
openai blog
google research
Jay Alammar
inFERENCe
mlif
VGEL Blog
Jay Mody
Finbarr Timbers
Machine Learning Mastery
Polo Club
Communities
GroundZero AI
Eureka Labs
GPU MODE
Umar Jamil AI
The Neural Nest
TheBloke AI
fast.ai
Latent Space
Mech Interp
EleutherAI
Machine Learning
Deep Learning
LocalLLaMA
ChatGPT
Deep Learning
Books
Deep Learning - Ian Goodfellow
Understanding Deep Learning
Dive into Deep Learning
The Little Book of Deep Learning
Grokking Deep Learning
Practical Deep Learning for Coders - fastai
Meta Learning - How To Learn Deep Learning And Thrive In The Digital Age
David MacKay - Information Theory, Inference, and Learning Algorithms
Courses
DeepLearning.AI
NYU Deep Learning - Yann LeCun
The Complete Mathematics of Neural Networks and Deep Learning
Intro to Deep Learning - Sebastian Raschka
Practical Deep Learning for Coders - fastai
Full Stack Deep Learning - 2022
David MacKay - Information Theory, Pattern Recognition, and Neural Networks
UC Berkeley CS 182: Deep Learning
MIT - Introduction to Deep Learning
CS231n - Deep Learning for Computer Vision
CS224d: Deep Learning for Natural Language Processing
CNN
CNN from Scratch with pure Mathematical Intuition
Convolutional Neural Network (CNN): A Complete Guide
CNN Explainer
ConvNetJS - Deep Learning in your browser
Convolutional Neural Networks Explained (CNN Visualized)
CNNs from different viewpoints
Image Kernels
Visualizing what ConvNets learn
Convolutions in Image Processing
Understanding “convolution” operations in CNN
Convolutional Neural Networks, Explained
RNN
Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs
Understanding LSTM Networks
Predict Stock Prices Using RNN: Part 1
Recurrent Neural Networks (RNN) - Made With ML
RNNs and LSTMs - jurafsky, stanford
The Unreasonable Effectiveness of Recurrent Neural Networks - Karpathy
NLP from Scratch - PyTorch
LLM
Umar Jamil
Build a Large Language Model (From Scratch) - Sebastian Raschka
Create a Large Language Model from Scratch with Python - Tutorial by elliotarledge
Intro to Transformers (slides) - giffmana
[M2L 2024] Transformers - Lucas Beyer (giffmana)
TRANSFORMER EXPLAINER - Polo Club
The Illustrated GPT-2 (Visualizing Transformer Language Models)
Attention Is All You Need - Implementation
Linear Relationships in the Transformer's Positional Encoding
Implement and Train ViT From Scratch for Image Recognition - PyTorch
a smol course - huggingface
How I Studied LLMs in Two Weeks: A Comprehensive Roadmap
How Large Language Models work - From zero to ChatGPT
Building effective agents - Anthropic
LLM Visualization
nlp course - huggingface
Neural Networks: Zero to Hero
PyTorch
Zero to Mastery Learn PyTorch for Deep Learning - Daniel Bourke
Learn PyTorch for deep learning in a day. Literally. - Daniel Bourke
PyTorch internals - ezyang's blog
MiniTorch
PyTorch is dead. Long live JAX. - Blog
Inside the Matrix: Visualizing Matrix Multiplication, Attention and Beyond
Karpathy
Blog
Neural Networks: Zero to Hero
CS231n Winter 2016
CS231n Winter 2016 - Course Site
Eureka Labs AI
karpathy
3Blue1Brown
Neural Networks
Neural Networks
Suggested by 3B1B - Neural Networks and Deep Learning
Suggested by 3B1B - Calculus on Computational Graphs: Backpropagation
Suggested by 3B1B - Neural Networks Demystified
Suggested by 3B1B - Learning To See
Roadmap
A Short Chronology Of Deep Learning For Tabular Data
Understanding AI - Lee Robinson
Machine Learning & Data Analysis
Courses
MIT 6.034 Artificial Intelligence, Fall 2010
Cornell CS4780 Machine Learning for Decision Making SP17
Machine Learning in C
Andrew Ng's Machine Learning Collection
Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Applied Machine Learning 2020 by Andreas Mueller
Machine Learning Notebook
Made With ML
Books
Python for Data Analysis
ML Ops
MLOps Basics
How to Learn MLOps in 2024 [Courses, Books, and Other Resources]
MLOps guide - huyen
SVM
MIT - Learning: Support Vector Machines
Derive the Dual Formulation for Support Vector Machines
Support Vector Machines - compphysics
Machine Learning Lecture 14 "(Linear) Support Vector Machines" -Cornell CS4780 SP17
Support Vector Machines, Dual Formulation, Quadratic Programming & Sequential Minimal Optimization
Support Vector Machine in Javascript, Demo - Karpathy
Interactive demo of Support Vector Machines (SVM)
Cross Entropy
Cross Entropy and Log Likelihood
Things that confused me about cross-entropy
Understanding binary cross-entropy / log loss: a visual explanation
Feature Engineering
Feature Engineering - Kaggle
A Reference Guide to Feature Engineering Methods
Advanced Feature Engineering
Complete Guide to Feature Engineering: Zero to Hero
ML Code Challenges
UC Irvine Machine Learning Repository
Reinforcement Learning
Reinforcement Learning: An Introduction - Book - Sutton
DeepMind x UCL | Introduction to Reinforcement Learning 2015
Spinning Up - OpenAI
Algorithms for Reinforcement Learning - Book
CS 294: Deep Reinforcement Learning, Spring 2017
Reinforcement Learning: An Overview - Kevin P. Murphy
Hugging Face - Deep RL Course
GPU
Parallel Programming
Is Parallel Programming Hard, And, If So, What Can You Do About It
Intro to Parallel Programming
Programming Parallel Computers
Python Parallel Computing
NHR@FAU - Parallel Programming
Parallel Programming - Leonardo Araujo Santos
CUDA
How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: a Worklog
CUDA Programming Course - High-Performance Computing with GPUs - elliotarledge
GPU(CUDA) MODE
Programming Massively Parallel Processors
CUDA Toolkit Documentation
CUDA C++ Programming Guide
Horace He - Making Deep Learning Go Brrrr
Tools and Unsorted
Netron - Deep Learning Model Inspector
Insights | Internet's Best Resources
Advanced NumPy
An Illustrated Guide to Shape and Strides
smolorg - maharshi
The Matrix Calculus You Need For Deep Learning
Fast Multidimensional Matrix Multiplication on CPU from Scratch
Computer Vision Papers
ml stash
Seeing Theory - A visual introduction to probability and statistics
Math & Data Science Digital Library - aarish22
Matrix Visualizer
The Matrix Cookbook