Books
- The Deep Learning Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
- Deep Reinforcement Learning Hands On by Maxim Lapan
- Distributed Machine Learning Patterns by Yuan Tang
- The Hundred-Page Machine Learning Book by Andriy Burkov
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- An Introduction to Statistical Learning with Applications in Python by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- Computer Age Statistical Inference by Bradley Efron and Trevor Hastie
- Probabilistic Programming & Bayesian Methods for Hackers by Cameron Davidson-Pilon
- Think Bayes by Allen B. Downey
- Information Theory, Inference, and Learning Algorithms by David J.C. MacKay
- Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams
- Data Intensive Text Processing w/ MapReduce by Jimmy Lin and Chris Dyer
- Mining Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman
- A First Encounter with Machine Learning by Max Welling
- Pattern Recognition and Machine Learning by Christopher Bishop
- Machine Learning & Bayesian Reasoning by David Barber
- Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan
- A Probabilistic Theory of Pattern Recognition by Luc Devroye, László Györfi, and Gábor Lugosi
- Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze
- Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
- Practical Artificial Intelligence Programming in Java by Mark Watson
- A Quest for AI by Nils J. Nilsson
- Introduction to Applied Bayesian Statistics and Estimation for Social Scientists by Scott M. Lynch
- Bayesian Modeling, Inference and Prediction by David Draper
- A Course in Machine Learning by Hal Daumé III
- Machine Learning, Neural and Statistical Classification by D. Michie, D.J. Spiegelhalter, and C.C. Taylor
- Bayesian Reasoning and Machine Learning by David Barber
- R Programming for Data Science by Roger D. Peng
- Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, and Mark A. Hall
- Machine Learning with TensorFlow by Nishant Shukla
- Hands‑On Machine Learning with Scikit‑Learn and TensorFlow by Aurélien Géron
- R for Data Science by Hadley Wickham and Garrett Grolemund
- Advanced R by Hadley Wickham
- Graph-Powered Machine Learning by Alessandro Negro
- Machine Learning for Dummies by John Paul Mueller and Luca Massaron
- Grokking Machine Learning by Luis G. Serrano
- Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
- Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David
- Fighting Churn With Data by Carl Gold
- Machine Learning Bookcamp by Alexey Grigorev
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
- Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur
- MLOps Engineering at Scale by Carl Osipov
- AI-Powered Search by Trey Grainger, Doug Turnbull, and Max Irwin
- Ensemble Methods for Machine Learning by Gautam Kunapuli
- Machine Learning Engineering in Action by Ben Wilson
- Privacy-Preserving Machine Learning by J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera
- Automated Machine Learning in Action by Qingquan Song, Haifeng Jin, and Xia Hu
- Human-in-the-Loop Machine Learning by Robert (Munro) Monarch
- Feature Engineering Bookcamp by Mauricio Aniche
- Metalearning: Applications to Automated Machine Learning and Data Mining by Pavel Brazdil, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren
- Managing Machine Learning Projects by Simon Thompson
- Causal Machine Learning by Robert Ness
- Bayesian Optimization in Action by Quan Nguyen
- Machine Learning Algorithms in Depth by Vadim Smolyakov
- Optimization Algorithms by Alaa Khamis
- Practical Gradient Boosting by Guillaume Saupin
- Machine Learning System Design by Valerii Babushkin and Arseny Kravchenko
- Fight Fraud with Machine Learning by Ashish Ranjan Jha
- Machine Learning for Drug Discovery by Noah Flynn
- Probabilistic Machine Learning: An Introduction by Kevin P. Murphy (2022)
- Probabilistic Machine Learning: Advanced Topics by Kevin P. Murphy (2023)
- Deep Learning with Python by François Chollet
- Deep Learning with Python, Second Edition by François Chollet
- Deep Learning with Python, Third Edition by François Chollet
- Deep Learning with JavaScript by Shanqing Cai, Stan Bileschi, and Eric D. Nielsen
- Grokking Deep Learning by Andrew Trask
- Deep Learning for Search by Tommaso Teofili
- Deep Learning and the Game of Go by Kevin Ferguson and Max Pumperla
- Machine Learning for Business by Doug Hudgeon and Richard Nichol
- Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina
- Deep Learning with Structured Data by Mark Ryan
- Inside Deep Learning by Edward Raff
- Math and Architectures of Deep Learning by Krishnendu Chaudhury
- Deep Learning for Natural Language Processing by Stephan Raaijmakers
- Deep Learning with R, Third Edition by François Chollet, Tomasz Kalinowski, and J.J. Allaire
- Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper
- Foundations of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze
- Natural Language Processing in Action by Hobson Lane, Cole Howard, and Hannes Max Hapke
- Natural Language Processing in Action, Second Edition by Hobson Lane, Maria Dyshel, and Hannes Max Hapke
- Real-World Natural Language Processing by Masato Hagiwara
- Essential Natural Language Processing by Edward Loper (and others? – early access)
- Getting Started with Natural Language Processing by Ekaterina Kochmar
- Transfer Learning for Natural Language Processing by Paul Azunre
- Neural Networks and Deep Learning by Michael Nielsen
- Graph Neural Networks in Action by Timo Korthals
- Think Stats by Allen B. Downey
- From Algorithms to Z-Scores by Norm Matloff
- The Art of R Programming by Norman Matloff
- Introduction to Statistical Thought by Michael Lavine
- Basic Probability Theory by Robert B. Ash
- Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
- Introduction to Probability by Dimitri P. Bertsekas and John N. Tsitsiklis
- Introduction to Probability and Statistics Using R by G. Jay Kerns
- Practical Regression and Anova using R by Julian Faraway
- R Practicals by Charles DiMaggio
- The R Inferno by Patrick Burns
- Probability Theory: The Logic of Science by E. T. Jaynes
- The Matrix Cookbook by Kaare Brandt Petersen and Michael Syskind Pedersen
- Linear Algebra by Georgi E. Shilov
- Linear Algebra Done Wrong by Sergei Treil
- Linear Algebra, Theory, and Applications by Kenneth Kuttler
- Convex Optimization by Stephen Boyd and Lieven Vandenberghe
- Calculus Made Easy by Silvanus P. Thompson
- Calculus by Ron Larson
- Active Calculus by Matt Boelkins
- AI Engineering: Building Applications with Foundational Models by Chip Huyen
- Introduction to Machine Learning Interviews by Chip Huyen
- Data Structures and Algorithms in Python by Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser
- Designing Data-Intensive Applications by Martin Kleppmann
- Designing Machine Learning Systems by Chip Huyen
- Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
- You Look Like a Thing and I Love You by Janelle Shane
- Machine Learning Yearning by Andrew Ng
- First Contact with TensorFlow by Jordi Torres
- Deep Learning with Python (Jason Brownlee) by Jason Brownlee
- TensorFlow for Machine Intelligence by Sam Abrahams, Danijar Hafner, Erik Erwitt, and Ariel Scarpinelli
- Getting Started with TensorFlow by Giancarlo Zaccone
- Building Machine Learning Projects with TensorFlow by Rodolfo Bonnin
- Deep Learning using TensorLayer by Hao Dong, Zihao Wang, Jiajun Su, and Jian Tang
- TensorFlow 2.0 in Action by Thushan Ganegedara
- Generative AI with LangChain by Ben Auffarth (accompanied by GitHub repo)
- Build a Large Language Model (From Scratch) by Sebastian Raschka
- Build GPT: How AI Works by (unknown author – listed as “How AI Works”)
- Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst
- Building LLMs for Production by (author not specified in link)
- Taming LLMs by (author not specified)
- Predictive Models: Explore, Explain, and Debug by Przemyslaw Biecek and Tomasz Burzykowski (2019)
- Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller (2019)
- Machine Learning Interpretability with H2O Driverless AI by Patrick Hall, Navdeep Gill, Megan Kurka, and Wen Phan (2018)
- An Introduction to Machine Learning Interpretability by Navdeep Gill and Patrick Hall (2018)
- Interpretable Machine Learning by Christoph Molnar (2018)
- NLP with PyTorch by Delip Rao and Brian McMahan
- Text Mining with R by Julia Silge and David Robinson
- Practical Natural Language Processing by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana
- Natural Language Processing with Spark NLP by Alex Thomas
- Deep Learning: A Visual Approach by Andrew Glassner
- Practical Deep Learning: A Python-Based Introduction by Ron Kneusel
- Dive Into Data Science by Bradford Tuckfield
- Math for Deep Learning by Ron Kneusel
- Why Machines Learn by Anil Ananthaswamy
- Understanding Deep Learning by Simon J.D. Prince
- Practical Deep Learning for Coders by Jeremy Howard and Sylvain Gugger
- The Shape of Data by Colleen M. Farrelly and Yaé Ulrich Gaba
- The Art of Machine Learning by Norman Matloff
- How AI Works by Ronald T. Kneusel
- Superintelligence by Nick Bostrom
- The Myth of Artificial Intelligence by Erik J. Larson
- The Coming Wave by Mustafa Suleyman
- Ways of Being by James Bridle
- Markov Decision Processes: Discrete Stochastic Dynamic Programming by Martin L. Puterman
- Reinforcement Learning and Optimal Control by Dimitri P. Bertsekas
- Graph Representation Learning by William L. Hamilton
- Network Science by Albert-László Barabási
- Blueprints for Text Analytics Using Python by Jens Albrecht, Sidharth Ramachandran, and Christian Winkler
- Natural Language Processing with PyTorch by Delip Rao and Brian McMahan
- Python Natural Language Processing by Jalaj Thanaki
- Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur
- Natural Language Processing: Python and NLTK by Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, and Iti Mathur
- Applied Text Analysis with Python by Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda
- Applied Natural Language Processing With Python by Taweh Beysolow II (2018)
- Deep Learning with Text by Thushan Ganegedara
- Taming Text by Grant S. Ingersoll, Thomas S. Morton, and Andrew L. Farris
- Foundations of Statistical Natural Language Processing (Manning/Schütze) by Christopher D. Manning and Hinrich Schütze
- Language Processing with Perl and Prolog by Pierre M. Nugues
- Handbook of Natural Language Processing by Nitin Indurkhya and Fred J. Damerau (eds.)
- Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications by Gary Miner, John Elder IV, Andrew Fast, Thomas Hill, Robert Nisbet, and Dursun Delen
- Fundamentals of Predictive Text Mining by Sholom M. Weiss, Nitin Indurkhya, and Tong Zhang
- Mining the Social Web by Matthew A. Russell and Mikhail Klassen
- Neural Network Methods for Natural Language Processing by Yoav Goldberg
- Text Mining: A Guidebook for the Social Sciences by Gabe Ignatow and Rada F. Mihalcea
- Practical Text Analytics by Steven Struhl
- Machine Learning for Text by Charu C. Aggarwal (2018)
- Natural Language Processing in Spanish by (multiple authors – IAA book)
- Foundations of Computational Linguistics by Roland Hausser
- Statistical Methods for Speech Recognition by Frederick Jelinek
Courses
- Udemy: Deep Learning and NLP A-Z™: How to create a ChatBot
- Udemy: Natural Language Processing with Deep Learning in Python
- Udemy: NLP - Natural Language Processing with Python
- Udemy: Deep Learning: Advanced NLP and RNNs
- Udemy: Natural Language Processing and Text Mining Without Coding
- CS224d: Deep Learning for Natural Language Processing (Stanford) – Reddit follow-along
- Coursera: Natural Language Processing courses
- Coursera: Applied Text Mining in Python
- Coursera: Sequence Models for Time Series and Natural Language Processing
- Coursera: Clinical Natural Language Processing
- DataCamp: Natural Language Processing Fundamentals in Python
- DataCamp: Sentiment Analysis in R: The Tidy Way
- DataCamp: Text Mining: Bag of Words
- DataCamp: Building Chatbots in Python
- DataCamp: Advanced NLP with spaCy
- Deep Learning Drizzle – curated list of lectures
- DeepMind x UCL: Introduction to Machine Learning & AI
- Yandex School of Data Analysis
- CMU Language and Statistics II: Empirical Methods in Natural Language Processing
- Columbia: COMS W4705 Natural Language Processing
- Columbia: COMS E6998 Machine Learning for Natural Language Processing (Spring 2012)
- Machine Translation: Spring 2016
- Commonlounge: Learn Natural Language Processing: From Beginner to Expert
- Big Data University: Advanced Text Analytics – Getting Results with SystemT
- Udacity: Natural Language Processing Nanodegree
- edX: Natural Language Processing – by Microsoft
- Data Scientist with R (DataCamp track)
- Data Scientist with Python (DataCamp track)
- MIT OCW: Genetic Algorithms
- AI Expert Roadmap
- edX: Convex Optimization
- Caltech: Learning from Data – by Yaser Abu-Mostafa
- Kaggle Learn – Data Science, ML, Python
- WhyLabs: Introduction to Monitoring ML – root‑cause production ML issues
- Weights & Biases: Effective MLOps – Model Development
- Scaler: Python for Data Science
- MLSys-NYU-2022 (Machine Learning in Finance)
- Hands-on Train and Deploy ML (crypto price prediction)
- LLM University (Cohere)
- DeepLearning.AI: Prompting for Vision Models
- IBM SkillsBuild: Data Science Course
- Coursera: Introduction to Data Science
- Coursera: Data Science Specialization (Johns Hopkins)
- Coursera: Data Mining Specialization
- Harvard CS109 Data Science
- OpenIntro
- Harvard CS171 Visualization
- Coursera: Process Mining: Data science in Action
- Oxford Deep Learning – YouTube playlist
- Oxford Machine Learning
- UBC Machine Learning – Nando de Freitas lectures
- Data Science Specialization GitHub repo
- Coursera Big Data Specialization
- edX: Statistical Thinking for Data Science and Analytics
- Cognitive Class AI by IBM
- Udacity: Intro to TensorFlow for Deep Learning
- Microsoft Professional Program for Data Science
- COMP3222/COMP6246 – Machine Learning Technologies (University of Southampton)
- Coursera TensorFlow in Practice
- 365 Data Science Course
- Coursera Natural Language Processing Specialization
- Coursera GAN Specialization
- Codecademy Data Science path
- MIT 18.06SC Linear Algebra (Gilbert Strang)
- MIT RES.18-010: A 2020 Vision of Linear Algebra (G. Strang)
- Intellipaat: Python for Data Science (free training)
- Coursera: Data Science – Statistics & Machine Learning
- Coursera: Recommender Systems Specialization (University of Minnesota)
- Stanford Artificial Intelligence Professional Program
- DataCamp: Data Scientist with Python career track
- Udemy: Programming with Julia
- LabEx Data Science Skill Tree
- CodeKidz: Data Science for Beginners (AI tutor)
- CodeKidz: Machine Learning for Beginners (AI tutor)
- Mathematics for Machine Learning – e‑resources compilation
- 3Blue1Brown: Essence of Linear Algebra
- 3Blue1Brown: Essence of Calculus
- 3Blue1Brown: Differential Equations
- Gilbert Strang: Linear Algebra vs Calculus (video)
- Basics of Integral Calculus in Tamil
- Fast.ai: Computational Linear Algebra
- Coursera: Machine Learning by Andrew Ng (Stanford)
- DataCamp: Data Engineer with Python
- Udacity: Intro to Machine Learning
- End-to-End Machine Learning (teachable)
- NVIDIA Deep Learning Institute
- Fast.ai: Introduction to Machine Learning for Coders
- Fast.ai
- Stanford CS221: Artificial Intelligence – Principles and Techniques
- Stanford CS229: Machine Learning (Andrew Ng)
- Stanford CS230: Deep Learning (Andrew Ng)
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition
- Stanford CS224n: Natural Language Processing with Deep Learning
- Stanford CS234: Reinforcement Learning
- Stanford CS330: Deep Multi‑task and Meta Learning
- Stanford CS25: Transformers United
- CMU 11-711: Advanced NLP (Graham Neubig)
- CMU 11-747: Neural Networks for NLP (Graham Neubig)
- CMU 11-737: Multilingual NLP (Graham Neubig)
- CMU 11-777: Multimodal Machine Learning (Louis-Philippe Morency)
- CMU 11-785: Introduction to Deep Learning (Bhiksha Raj)
- CMU Low Resource NLP Bootcamp 2020 (Graham Neubig)
- MIT 6.S191: Introduction to Deep Learning
- MIT 6.S094: Deep Learning (Lex Fridman)
- MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity
- UCL COMP M050: Reinforcement Learning (David Silver)
- YouTube: Machine Learning by StatQuest with Josh Starmer
- YouTube: Intelligence and Learning by The Coding Train
- Kaggle: Intro to Deep Learning
- Digital Trends: What is an artificial neural network?
- Medium: Everything You Need to Know About Artificial Neural Networks
- History of the Perceptron
- Towards Data Science: McCulloch-Pitts Neuron
- Mind.ilstu: McCulloch-Pitts Neurons
- Skynet Today: A Brief History of Neural Nets and Deep Learning
- Dive into Deep Learning (d2l.ai)
- Neural Networks and Deep Learning (Michael Nielsen)
- Stanford CS229 Deep Learning CheatSheet
- YouTube: Information Theory, Pattern Recognition and Neural Networks
- Tutorialspoint: Artificial Neural Networks
- Analytics Vidhya: How ANN works simplified
- Good Audience: Artificial Neural Networks Explained
- Basic Introduction to Neural Networks (U. Wisconsin)
- Medium: Deep Learning’s Mathematics
- NVIDIA: Artificial Neural Network
- Becoming Human: Artificial Neuron Networks Basics
- Saed Sayad: Artificial Neural Network
- Hackernoon: ANN – Beginning of the AI revolution
- Intro to ANN (U. Mass Lowell)
- Stanford CS231n course notes (Convolutional Neural Networks)
- Tutorialspoint: Artificial Neural Network (full tutorial)
- Adventures in Machine Learning: Neural Networks Tutorial
- Guru99: Neural Network Tutorial
- How to Build a Neural Network (Steven Miller)
- Edureka: Neural Network Tutorial
- Ujjwal Karn: Quick Intro to Neural Networks
- AI Junkie: Neural Networks in Plain English
- Awesome Deep Learning (GitHub)
- Awesome Deep Learning Papers (GitHub)
- Geoffrey Hinton’s Publications
- Awesome Recurrent Neural Networks (GitHub)
- TensorFlow Neural Network Playground
- Colah’s blog: Neural Networks, Manifolds, and Topology
- A Brief Introduction to Neural Networks (pdf)
- Introduction to ANN (Univ. Nevada)
- Basics of ANN (IJCSMC paper)
- Haykin: Neural Networks and Learning Machines (pdf)
- Artificial Neural Networks: A Tutorial (LSU)
- An Introduction to Neural Networks (Iowa State)
- Ronan Collobert: Neural Networks slides
- Neural Network Design (Hagan et al.)
- Artificial Neural Networks for Beginners (Carlos Gershenson)
- Neural Networks: A Systematic Introduction (Raúl Rojas)
- Neural Networks and Introduction to Deep Learning (Besse)
- Victor Zhou: An Introduction to Neural Networks with Python
- Builtin: Lightning‑Fast Introduction to Deep Learning and TensorFlow 2.0
- Stanford Deep Learning Tutorial: Convolutional Neural Network
- Ujjwal Karn: An Intuitive Explanation of Convolutional Neural Networks
- Brains to Bytes: Deep Learning Mini‑Course
- Full Stack Deep Learning (fall 2019)
- Manning: Math and Architectures of Deep Learning
- Machine Learning & Deep Learning Fundamentals (YouTube playlist)
- IIT Kharagpur: Artificial Neural Networks (YouTube playlist)
- YouTube: A Friendly Introduction to Deep Learning
- YouTube: Neural Networks Explained (3Blue1Brown)
- YouTube: Beginner Intro to Neural Networks (David Miller)
- YouTube: Neural Network that Changes Everything
- YouTube: How Deep Neural Networks Work (Brandon Rohrer)
- YouTube: Deep Learning (Computerphile)
- YouTube: How Convolutional Neural Networks Work
- YouTube: Neural Network Programming (deeplizard)
- Welch Labs: Neural Networks Demystified
- YouTube: Getting Started with TensorFlow and Deep Learning
- YouTube: Deep Learning with TensorFlow (playlist)
- YouTube: Neural Network 3D Simulation
- YouTube: Geoffrey Hinton – The Foundations of Deep Learning
- YouTube: Deep Learning – A Crash Course
- YouTube: Neural Networks from Scratch (sentdex)
- NYU Deep Learning 2020 (YouTube playlist)
- Stanford CS224n course website
- DeepLearning.AI: Generative AI for Everyone (Andrew Ng)
- DeepLearning.AI: LLM series of courses
- ACL 2023 Tutorial: Retrieval-based Language Models and Applications
- Microsoft: State of GPT (video)
- Tsinghua NLP (Liu Zhiyuan) – Large Model public class (Bilibili)
- Stanford CS25: Transformers United V4
- Johns Hopkins CS 601.471/671: Self-supervised Models
- Hung-yi Lee (NTU): GenAI course
- OpenAI Cookbook (examples and guides)
- Hands on LLMs (GitHub – real‑time financial advisor)
- University of Waterloo CS 886: Recent Advances on Foundation Models
- DeepLearning.AI: Getting Started with Mistral
- Coursera: Prompt Engineering for ChatGPT
- LangGPT – empower everyone to be a prompt expert
- Mistral AI Cookbook
- NTU: Introduction to Generative AI 2024 Spring
- Andrej Karpathy: build‑nanoGPT (video+code)
- Andrej Karpathy: LLM101n – build a Storyteller
- Datawhale: LLMs From Scratch (Chinese version)
- OpenRAG (Notion site)
- Way to AGI (Feishu wiki)
- Andrej Karpathy: Neural Networks – Zero to Hero (YouTube playlist)
- Transformer Explainer (interactive visualization)
- andysingal/llm-course (GitHub)
- LM‑class.org (lectures)
- Google Cloud Skills Boost: Generative AI for Developers
- Anthropic: Prompt Engineering Interactive Tutorial
- Cohere LLM University
- Ambuj Tewari: LLMs and Transformers (course)
- Smol Vision – recipes for small vision models
- DeepLearning.AI: Multimodal RAG – Chat with Videos
- LLMs Interview Note (GitHub)
- Weights & Biases: RAG++ – From POC to production
- Weights & Biases AI Academy (finetuning, LLM courses)
- Prompt Engineering & AI tutorials & Resources
- YouTube: Learn RAG From Scratch – Python AI Tutorial (LangChain Engineer)
- HuggingFace Learn
- Andrej Karpathy: Deep Dive into LLMs like ChatGPT (video)
- LLM Technology Popularization (GitHub)
- Stanford CS234: Reinforcement Learning (course website)
- IIT Madras CS230: Reinforcement Learning (video)
- Mila: Excursions in Reinforcement Learning
- RL‑Book supplementary materials
- Deep RL Bootcamp (lectures)
- UC Berkeley CS285: Deep Reinforcement Learning
- OpenAI Spinning Up in Deep RL
- Imitation Learning for Robotics (U. Toronto)
- Stanford CS330: Deep Multi‑Task and Meta Learning (course website)
- Depth First Learning: Trust Policy Optimisation series
- IIT Madras CS7015: Deep Learning
- Stanford CS230: Deep Learning (course website)
- Stanford CS229: Machine Learning (course website)
- MIT OCW: 6.007 Signals and Systems
- MIT OCW: 6.008 Digital Signal Processing
- MIT 6.437/6.438 Algorithms for Inference / Random Processes
- Coursera: EPFL Digital Signal Processing (DSP)
- OSSU: Linear Algebra for DSP foundation (YouTube)
- Steve Brunton – Control & Signal Processing Bootcamp (YouTube)
- Brian Douglas – Control Systems & DSP Concepts (YouTube)
- KVRAudio – Audio DSP Tutorials (forum + free book)
- PySDR – Practical RF & Comms DSP (Python SDR projects)
- Stanford CS224d: Lecture Notes – neural network basics (PDF)
- Stanford CS224d: Lecture Notes – RNN, LSTM, GRU (PDF)
- Oxford Deep Learning (Nando de Freitas) – Lecture 12: RNNs and LSTMs
- Oxford Deep Learning – Lecture 13: Alex Graves on Hallucination with RNNs
- NYU Deep Learning – Lecture: Graph Convolutional Networks (Xavier Bresson)
- Penn: Graph Neural Networks course
- Applied LLMs Mastery 2024 (Notion course website) – Registration form
- ETH Zurich: Large Language Models (spring 2023)
- Princeton: Understanding Large Language Models (fall 2022)
- Hugging Face: Transformers Course
- Stanford CS324 – Large Language Models (winter 2022)
- Coursera: Generative AI with Large Language Models (deeplearning.ai)
- Coursera: Introduction to Generative AI
- Google Cloud: Generative AI Fundamentals
- Google & Kaggle: 5‑Day Gen AI Intensive Course (YouTube)
- Google Cloud: Introduction to Large Language Models
- Google Cloud: Introduction to Generative AI
- DataCamp: Generative AI Concepts (Daniel Tedesco, Google)
- WeCloudData: 1 Hour Introduction to LLM (video)
- Databricks: LLM Foundation Models from the Ground Up (playlist)
- NVIDIA: Generative AI Explained
- Google Cloud: Transformer Models and BERT Model
- AWS: Generative AI Learning Plan for Decision Makers
- Google Cloud: Introduction to Responsible AI
- Microsoft Azure: Fundamentals of Generative AI
- Microsoft: Generative AI for Beginners (GitHub)
- Udemy: ChatGPT for Beginners – The Ultimate Use Cases for Everyone
- Andrej Karpathy: Intro to Large Language Models (1‑hour talk)
- Learn Prompting: ChatGPT for Everyone
- Kshitiz Verma: Large Language Models (LLMs) (English playlist)
- CodeKidz: Generative AI for Beginners (based on Microsoft course)
- Udacity: LLMOps – Building Real‑World Applications with LLMs
- Full Stack Deep Learning: LLM Bootcamp
- edX (Databricks): Large Language Models – Application through Production
- AWS: Generative AI Foundations (YouTube playlist)
- ineuron: Introduction to Generative AI Community Course (YouTube)
- Lightning AI: LLM Learning Lab
- DeepLearning.AI: LangChain for LLM Application Development
- DeepLearning.AI: LLMOps
- DeepLearning.AI: Automated Testing for LLMOps
- AWS: Building Generative AI Applications Using Amazon Bedrock
- DeepLearning.AI: Efficiently Serving LLMs
- DeepLearning.AI: Building Systems with the ChatGPT API
- DeepLearning.AI: Serverless LLM apps with Amazon Bedrock
- DeepLearning.AI: Building Applications with Vector Databases
- DeepLearning.AI: Automated Testing for LLMOps (short course)
- DeepLearning.AI: Build LLM Apps with LangChain.js
- DeepLearning.AI: Advanced Retrieval for AI with Chroma
- Coursera: Operationalizing LLMs on Azure
- freeCodeCamp: Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone
- Activeloop: Training & Fine‑Tuning LLMs for Production
- DeepLearning.AI: Reinforcement Learning from Human Feedback (RLHF)
- DeepLearning.AI: Finetuning Large Language Models
- DeepLearning.AI: LangChain – Chat with Your Data
- DeepLearning.AI: Prompt Engineering with Llama 2
- DeepLearning.AI: ChatGPT Prompt Engineering for Developers
- LlamaIndex: Advanced RAG Orchestration series (YouTube)
- Coursera: Prompt Engineering Specialization
- NVIDIA: Augment your LLM Using Retrieval Augmented Generation
- DeepLearning.AI: Knowledge Graphs for RAG
- DeepLearning.AI: Open Source Models with Hugging Face
- DeepLearning.AI: Vector Databases – from Embeddings to Applications
- DeepLearning.AI: Understanding and Applying Text Embeddings (Google Vertex AI)
- DeepLearning.AI: JavaScript RAG Web Apps with LlamaIndex
- DeepLearning.AI: Quantization Fundamentals with Hugging Face
- DeepLearning.AI: Preprocessing Unstructured Data for LLM Applications
- Activeloop: Retrieval Augmented Generation for Production (LangChain & LlamaIndex)
- DeepLearning.AI: Quantization in Depth
- DeepLearning.AI: Building and Evaluating Advanced RAG Applications
- DeepLearning.AI: Evaluating and Debugging Generative AI Models (Weights & Biases)
- DeepLearning.AI: Quality and Safety for LLM Applications
- DeepLearning.AI: Red Teaming LLM Applications
- DeepLearning.AI: How Diffusion Models Work
- YouTube: How to Use Midjourney, AI Art and ChatGPT to Create an Amazing Website (Brad Hussey)
- Scrimba: Build AI Apps with ChatGPT, DALL‑E and GPT‑4
- Carnegie Mellon: 11‑777 Multimodal Machine Learning (YouTube playlist)
- NVIDIA: Building RAG Agents with LLMs
- DeepLearning.AI: Functions, Tools and Agents with LangChain
- DeepLearning.AI: AI Agents in LangGraph
- DeepLearning.AI: AI Agentic Design Patterns with AutoGen
- DeepLearning.AI: Multi AI Agent Systems with crewAI
- DeepLearning.AI: Building Agentic RAG with LlamaIndex
- Arize AI: LLM Observability – Agents, Tools, and Chains
- AWS Developers: Agents Tools & Function Calling with Amazon Bedrock (YouTube)
- Coursera: ChatGPT & Zapier – Agentic AI for Everyone
- Manning: Multi‑Agent Systems with AutoGen (book)
- LLM Agents MOOC Fall 2024 (Dawn Song & Xinyun Chen)
- UC Berkeley CS294/194-196: Large Language Model Agents
- Coursera: Avoiding AI Harm
- Coursera: Developing AI Policy
- NVIDIA: Getting Started with Deep Learning (short intro)
- MIT Deep Learning Bootcamp (free, intensive)
- Fast.ai: Practical Deep Learning for Coders (course videos)
- C++ Neural Network in a Weekend (blog tutorial)
- Hugging Face: smolagents course (agentic AI)
- ColumbiaX: Artificial Intelligence (edX)
- ColumbiaX: Machine Learning (edX)
- Coursera: Deep Learning Specialization (Andrew Ng, deeplearning.ai)
- Stanford CS20: TensorFlow for Deep Learning Research
- Deep Learning MOOC (various)
- Full-Stack Deep Learning (course)
- Explained.ai: Visual, interactive explanations of ML concepts
- Coursera: Machine Learning Specialization (University of Washington)
- University of Oxford Machine Learning (Nando de Freitas, 2014-15)
- Coursera: Reinforcement Learning Specialization (University of Alberta)
- YouTube: DeepMind x UCL Reinforcement Learning (David Silver) – slides
- Manning: Keras in Motion (paid)
- Stanford CS231n: CNNs for Visual Recognition (Spring 2017)
- UC Berkeley CS294: Deep Reinforcement Learning (Fall 2017) – course website
- Udacity: Machine Learning (Georgia Tech)
- Udacity: Reinforcement Learning (Georgia Tech)
- Udacity: Machine Learning for Trading
- YouTube: Mining of Massive Datasets (Stanford) – MOOC
- Google: Machine Learning Crash Course
- LambdaSchool: Machine Learning Mini Bootcamp (free and paid)
- Microsoft Professional Program for Artificial Intelligence
- Open Machine Learning Course (GitHub) – articles on Medium
- Udemy: Machine Learning A‑Z (Hands‑On Python & R)
- Manning: Deep Learning Crash Course (paid)
- Manning: Reinforcement Learning in Motion (paid)
- Pluralsight: Deep Learning with Keras (paid)
- Khan Academy: Statistics and Probability
- Manning: Grokking Artificial Intelligence Algorithms (paid)
- Google AI Education
- Kaggle: Learn (free certificates)
- Cognitive Class: Machine Learning with Python
- Cognitive Class: Intro to Data Science
- Dataquest: Data Scientist in Python (free and paid)
- Manning: Interpretable AI (book)
- Manning: Deploying a Deep Learning Model on Web and Mobile (liveProject)
- Scaler: Complete Data Science and ML Course (paid)
- Arize AI: ML Observability Fundamentals (free)
- MIT: Introduction to Data‑Centric AI
- Brainalyst: Data science course with placement
- Brainalyst: Data Visualization Course
- Brainalyst: Data Visualization Python Course
- Brainalyst: Data Science with R Programming
- Brainalyst: Data Science with Python Course
- Brainalyst: Data Science 360 Training Course
- Brainalyst: Big Data & Cloud Computing Course
- Brainalyst: Full Stack Data Science Program
- Coursera: Mathematics for Machine Learning Specialization (Imperial College London)
- Coursera: Machine Learning Engineering for Production (MLOps) Specialization (DeepLearning.ai)
- Comet: LLMOps – Building Real‑World Applications with LLMs (free)
- Scaler: Data Science Machine Learning Course (paid)
- LabEx: Machine Learning Skill Tree (hands‑on labs)
Datasets
- Kaggle Datasets: Various / General ML
- CelebA: Computer Vision / Facial Attributes
- COCO: Computer Vision / Object Detection
- ImageNet: Computer Vision / Classification
- Cityscapes Dataset: Computer Vision / Segmentation
- ObjectNet: Computer Vision / Robustness Testing
- LAION 5B: Multimodal / Vision-Language
- NAIRR Datasets: Various / Research Datasets
- UCI Machine Learning Datasets: Traditional ML / Tabular
- Common Crawl: NLP / Web-Scale Corpus
- The Pile: NLP / Language Modeling
- C4 (Colossal Clean Crawled Corpus): NLP / Pretraining Corpus
- UCI’s Text Datasets: Collection of databases, domain theories, and data generators for ML
- data.world’s Text Datasets: Text mining datasets
- Awesome Public Datasets – Natural Language: Curated list of public NLP datasets
- Insight Resources Datasets: Datasets from University College Dublin
- Bing Sentiment Analysis: Sentiment analysis datasets
- Consumer Complaint Database: From the Consumer Financial Protection Bureau
- Sentiment Labelled Sentences Data Set: Sentences from imdb, amazon, yelp labelled positive/negative
- Amazon product data: Product reviews and metadata
- Data is Plural: Weekly collection of interesting datasets
- FiveThirtyEight’s datasets: Data from FiveThirtyEight articles
- R’s
datasets package: Built-in datasets for R
- 200,000 Russian Troll Tweets: Released by Congress from suspended Twitter accounts
- Wikipedia: List of datasets for ML research: Comprehensive Wikipedia list
- Kaggle: UMICH SI650 - Sentiment Classification: Sentiment classification dataset
- Lee’s Similarity Data Sets: Datasets for similarity judgment
- Corpus of Presidential Speeches (CoPS) and Clinton/Trump Corpus: Political speech corpora
- 15 Best Chatbot Datasets for Machine Learning: Curated list of chatbot datasets
- A Survey of Available Corpora for Building Data-Driven Dialogue Systems: PDF survey of dialogue corpora
- Hate-speech-and-offensive-language: Hate speech detection dataset
- First Quora Dataset Release: Question Pairs: Quora question pair dataset
- The Best 25 Datasets for Natural Language Processing: Curated list of NLP datasets
- SWAG: Large-scale dataset for Natural Language Inference (NLI) with common-sense reasoning
- MIMIC: Deidentified health data from ~40,000 critical care patients
- Clinical NLP Dataset Repository: Curated list of publicly-available clinical datasets for NLP research
- Million Song Lyrics: Lyrics for the Million Song Dataset
- The Multi-Genre NLI Corpus: Natural language inference corpus
- Twitter US Airline Sentiment: Twitter sentiment about US airlines
- DuoRC: 186K unique question-answer pairs for paraphrased reading comprehension
- EDGAR Financial Statements: Reporting engine for financial and regulatory filings (text mining)
- American National Corpus Download: Open American National Corpus
- Santa Barbara Corpus of Spoken American English: Spoken American English corpus
- Leipzig Corpora Collection: Corpora in English, Arabic, French, Russian, German
- Awesome Twitter Tools & Datasets: Twitter-related datasets and tools
- The Big Bad NLP Database: Collection of NLP datasets
- CBC News Coronavirus articles: COVID-19 news articles from CBC
- Hugging Face Dataset Viewer – Financial PhraseBank: Financial sentiment classification dataset
- OpenSLR: Open Speech and Language Resources
- VoxForge: Open source speech recognition corpus
- Flickr 8k: Image captioning dataset with 8,000 images
- Flickr 30k: Image captioning dataset with 30,000 images
- The bAbI Project: Datasets for text understanding and reasoning (QA, dialog, etc.)
- SQuAD: Stanford Question Answering Dataset
- NLVR: Natural Language for Visual Reasoning (built on NYU Depth v2)
- COCO-QA: Image QA dataset based on MSCOCO images
- DAQUAR: Dataset for visual question answering (based on MSCOCO)
- Multilingual Image QA: Image QA dataset by Baidu (Chinese with English translation)
- THUMOS: Large-scale action recognition dataset
- MultiTHUMOS: Extension of THUMOS ‘14 with dense multilabel annotation
Research Paper
- Awesome-LLM-Reasoning – Curated list of LLM reasoning papers and resources
- Awesome-LLM-Strawberry – Resources on LLM reasoning, especially o1-like models
- Awesome-LLM-Reasoning-Openai-o1-Survey – Survey of OpenAI o1 style reasoning
- awesome-o1 – Papers and code for System‑2 reasoning and o1
- system-2-research – Research on System‑2 reasoning in LLMs
- ninehills/blog/issues/121 – Blog post about o1 reasoning
- Open-O1 – Open source implementation of o1 reasoning
- O1-Journey – Training data and models for o1‑style reasoning
- show-me – Interactive tool to visualize chain‑of‑thought
- g1 – Groq‑based implementation of o1 reasoning
- Llamaberry-Chain-of-Thought-Reasoning-in-AI – Chain‑of‑thought reasoning with Llama
- open-strawberry – Open replication of OpenAI’s Strawberry (o1)
- Steiner Preview collection – Steiner reasoning models on HF
- LLaMA-O1 – LLaMA finetuned for o1 reasoning
- Skywork O1 Open collection – Skywork’s open o1 models
- QwQ collection – Qwen’s QwQ reasoning models
- skywork-o1-prm-inference – Process reward model inference for o1
- LLaVA-Reasoner-DPO – DPO fine‑tuning for vision‑language reasoning
- ADaM-BJTU – Organisation for reasoning and alignment research
- OpenRFT – Open‑source reasoning fine‑tuning framework
- Slow_Thinking_with_LLMs – Implementing slow thinking / System‑2 in LLMs
- Thinking-Claude – Prompting Claude to use chain‑of‑thought
- Art-v0-3B – Lightweight reasoning model (3B)
- DeepSeek-R1 – DeepSeek’s reasoning‑enhanced model
- DeepSeek-R1-Zero – RL‑trained reasoning model from scratch
- open-r1 – Hugging Face’s full replication of DeepSeek‑R1
- simpleRL-reason – Minimal RL for reasoning abilities
- TinyZero – Smaller scale replication of DeepSeek‑Zero
- Baichuan-M1-14B – Medical reasoning model from Baichuan
- open-r1-multimodal – Multimodal extension of Open‑R1
- open-thoughts – Open‑source reproduction of o1‑style reasoning
- Mini-R1 – Blog on training a tiny DeepSeek‑R1
- LLaMA-Berry – Paper: Pairwise optimization for o1‑like reasoning (arxiv)
- MCTS-DPO – Paper: MCTS combined with DPO for reasoning
- OpenR – Open‑source framework for reasoning agents
- arXiv 2410.02725 – Paper: Process reward models for o1
- LLaVA-o1 – Paper: o1‑style reasoning for vision‑language models
- Marco-o1 – Paper: Open‑domain reasoning with o1
- OpenAI o1 deliberative alignment report – OpenAI’s technical report on o1 alignment
- DRT-o1 – Dense reasoning tokens for o1
- Virgo – Paper: Verifiable reasoning for o1
- HuatuoGPT-o1 – Paper: Medical reasoning with o1
- o1 roadmap – Paper: Survey and roadmap for o1‑like models
- Mulberry – Paper: Multimodal reasoning with o1
- arXiv 2412.09413 – Paper: Efficient reasoning via step‑wise supervision
- arXiv 2501.02497 – Paper: Reinforcement learning for reasoning
- Search-o1 – Paper: Agentic search for reasoning
- arXiv 2501.18585 – Paper: Test‑time scaling for reasoning
- s1 (simple scaling) – Scaling test‑time compute for reasoning tasks
- R1-V – Vision reasoning with R1
- R1-Nature – Nature‑inspired reasoning strategies
- Logic-RL – Logic reasoning with reinforcement learning
- unlock-deepseek – Chinese guide to DeepSeek models
- LIMO – Less is more for reasoning (data efficiency)
- easy-r1 – Simplified training pipeline for R1‑style models
- nanoRLHF r1-v0 example – Minimal RLHF for reasoning
- R1-Multimodal-Journey – Multimodal extension of R1 reasoning
- X-R1 – eXplainable reasoning with R1
- deepscaler – Scaling reasoning with RL
- RAGEN – Reasoning‑augmented generation
- oat-zero – Zero‑shot reasoning via online adaptation
- lmm-r1 – Large multimodal model reasoning with R1
- OpenSeek – Open‑source search‑augmented reasoning
- ascend_r1_turtorial – Tutorial for Ascend NPU + R1 training
- VLM-R1 – Vision‑language model reasoning with R1
- diagnosis_zero – Zero‑shot medical diagnosis reasoning
- simple_GRPO – Minimal GRPO implementation for reasoning
- DeepSeekRL-Extended – Extended RL training for DeepSeek
- Open-R1-Video – Video reasoning with Open‑R1
- Open-Reasoner-Zero – From‑scratch reasoning agent
- Namo-R1 – Reasoning for embodied agents
- EasyR1 – User‑friendly R1 training framework
- R1-Onevision – Unified vision reasoning model
- Video-R1 – Temporal reasoning for videos
- TinyR1-32B-Preview – Small R1‑style model (32B)
- swe-rl – RL for software engineering reasoning
- VisualThinker-R1-Zero – R1‑like visual reasoning from scratch
- R1-Vision – R1 for image understanding tasks
- deepseek-r1-vision – Vision capabilities for DeepSeek‑R1
- Light-R1-32B – Lightweight R1‑style model
- Visual-RFT – Reinforcement fine‑tuning for vision reasoning
- GSM8K-RLVR – RL for GSM8K math reasoning
- MM-EUREKA – Multimodal reasoning via RL
- nanoGRPO – Minimal GRPO implementation
- Search-R1 – Reasoning with search as a tool
- GRPO-Training-Suite – Suite for GRPO training
- Seg-Zero – Zero‑shot segmentation via reasoning
- R1-Omni – Omni‑modal reasoning with R1
- OpenManus-RL – RL for generalist agent reasoning
- arXiv 2503.07536 – Paper: Lightweight reasoning distillation
- Vision-R1 – R1 for vision tasks
- MMR1 – Multimodal reasoning with R1
- CP-Zero – Zero‑shot critical path reasoning
- Skywork-R1V – R1 for vision‑language
- arXiv 2503.13939v1 – Paper: Long‑context reasoning
- Agent-R1 – R1 for agentic workflows
- Awaker2.5-R1 – Next‑gen reasoning model
- EXAONE-Deep – Deep reasoning from LG AI
- open-r1-reprod – Reproduction of Open‑R1
- Fin-R1 – Financial reasoning with R1
- understand-r1-zero – Analyzing emergent reasoning in R1‑Zero
- Efficient-R1-VLLM – Efficient R1 for large vision‑language models
- arXiv 2502.19655 – Paper: Reasoning with small models
- arXiv 2503.21620v1 – Paper: Reasoning verification
- arXiv 2503.16081 – Paper: Self‑evolving reasoning
- ADORA – Adaptive reasoning for agents
- Temporal-R1 – Temporal reasoning
- AReaL – Asynchronous RL for reasoning
- CPPO – Continual PPO for reasoning
- arXiv 2503.23829 – Paper: Process supervision for reasoning
- SEED-Bench-R1 – Reasoning benchmark
- nano-aha-moment – Emergent reasoning in small models
- Ocean-R1 – Ocean‑scale R1 for VLMs
- VideoChat-R1 – Video understanding + reasoning
- Seed-Thinking-v1.5 – ByteDance’s thinking model
- Skywork-OR1 – Open‑reasoning model series
- Kimi-VL – Kimi’s vision‑language reasoning
- arXiv 2504.08600 – Paper: Reasoning with synthetic data
- TinyLLaVA-Video-R1 – R1 for tiny video models
- arXiv 2504.11914 – Paper: Inference scaling for reasoning
- GRPO-Zero – GRPO from scratch
- PR1 – Process reward model for reasoning
- Agentic-RAG-R1 – R1 with retrieval‑augmented generation
- Tina – Tiny reasoning agent
- qwen-dianjin – Qwen’s reasoning model (Dianjin)
- RAGEN (RAGEN-AI) – Reasoning‑enhanced generation framework
- MiMo – Xiaomi’s reasoning model
- nano_rl – Minimal RL for reasoning experiments
- MINI_LLM – Train your own tiny LLM from scratch
- minimind – Minimalist LLM implementation
- baby-llama2-chinese – Baby Llama 2 for Chinese
- ChatLM-mini-Chinese – Minimal Chinese chat LLM
- tiny-llm-zh – Tiny LLM for Chinese
- build_MiniLLM_from_scratch – Step‑by‑step build a mini LLM
- TinyLlama – 1.1B Llama model for small footprint
- Zero-Chatgpt – Build a ChatGPT‑like model from zero
- nanotron-smol-cluster / cosmo-1b – Train 1B models on small clusters with Cosmopedia
- Phi2-mini-Chinese – Chinese version of Phi‑2
- OLMo – Open Language Model from AI2
- MicroLlama – Micro‑sized Llama variant
- Chinese-Tiny-LLM – Chinese‑focused tiny LLM
- MiniLLaMA3 – Mini version of LLaMA 3
- 1.5-Pints – 1.5B parameter pretrained model
- Steel-LLM – Efficient LLM for resource‑constrained devices
- YuLan-Mini – Miniature LLM from RUC
- smolGPT – Very small GPT implementation
- tiny-llm (skyzh) – Educational tiny LLM
- awesome-3D-generation – Curated list of 3D generation papers
- awesome-3d-human-reconstruction – Resources for 3D human reconstruction
- awesome-deepseek-integration – Integrations for DeepSeek models
- one-small-step – Collection of LLM technology resources (Chinese)
- Awesome-AITools – Curated list of AI tools
- ai-game-development-tools – AI tools for game dev
- StableHoudini – Stable Diffusion integration for Houdini
- chatgpt-guide – ChatGPT usage guide (Chinese)
- UnityInvokeAI – InvokeAI client for Unity
- awesome-ai-painting – AI painting tools and resources
- awesome-ai-art – AI art resources
- 最大的 AI 工具目录库 (Zhihu) – Largest AI tools directory (Chinese)
- Awesome-Diffusion-Models – Diffusion model papers and code
- stablediffusion WebUI guide – Comprehensive Stable Diffusion WebUI doc
- 元素法典—Novel AI 元素魔法全收录 – Novel AI prompt guide (Chinese)
- AI ART column (Zhihu) – AI art column on Zhihu
- AIConnectors – Unity connectors for AI APIs
- awesome-3dbody-papers – 3D body reconstruction papers
- Awesome-Text-to-Image – Text‑to‑image generation resources
- awesome-aigc – AIGC curated list
- Stable-Diffusion-Unity-Integration – Stable Diffusion in Unity
- ChatGPT-API-unity – ChatGPT API wrapper for Unity
- Prompt-Engineering-Guide-Chinese – Prompt engineering guide (Chinese)
- AI工具集 (aiyjs.com) – Chinese AI tools directory
- StableDiffusionBook – Book on Stable Diffusion (Chinese)
- minimind-v – Minimal vision‑language model
- zero_nlp / train_llava – Train LLaVA from zero
- Zero-Qwen-VL – Train Qwen VL from scratch
- MPP-LLaVA – Multi‑patch processing for LLaVA
- nanoLLaVA – Tiny LLaVA implementation
- TinyLLaVA_Factory – Factory for tiny LLaVA models
- TinyLLaVA-Video – Tiny LLaVA for video understanding
- tiny-qwen – Tiny version of Qwen
- smol-vision – Small vision‑language models recipes
- nanoVLM – Minimal VLM implementation from Hugging Face
- Standard Notations for Deep Learning (CS230) – Notation cheat sheet from Stanford
- AI Index Report (Stanford) – Annual report on AI trends
- Historical data on ‘notable’ Models by Epoch – Timeline of notable AI models
- Ethics of AI course – Free online course on AI ethics
- Monthly Best GenAI Papers List – Monthly curated LLM papers
- GenAI Interview Resources – Prep questions for GenAI interviews
- Applied LLMs Mastery 2024 course material – Full course on LLM applications
- Generative AI Genius 2024 course material – Advanced GenAI course
- List of all GenAI-related free courses (90+) – Over 90 free courses
- Code repositories/notebooks for GenAI applications – Practical GenAI notebooks
- Applied LLMs Mastery full course content – Complete 10‑week course
- 5‑day LLM foundations roadmap – Quick roadmap for LLM basics
- 60 Common GenAI Interview Questions – Q&A for interviews
- ICLR 2024 paper summaries (Notion) – Summaries of ICLR 2024 papers
- 3‑day RAG roadmap – Roadmap to learn RAG in 3 days
- 5‑day LLM agents roadmap – Become an LLM agent expert in 5 days
- Agents 101 guide – Introductory guide to AI agents
- Introduction to Multimodal LLMs – Basics of multimodal LLMs
- LLM Lingo Series – Definitions of common LLM terms
- Evaluating Explainable AI on a Multi‑Modal Medical Imaging Task (AAAI 2022) – XAI evaluation in medical imaging
- How can I choose an explainer? An Application‑grounded Evaluation (FAccT 2021) – Framework to select explanation methods
- Reasons, Values, Stakeholders: A Philosophical Framework for XAI (FAccT 2021) – Philosophical foundation for XAI
- Formalizing Trust in AI (FAccT 2021) – Prerequisites for human trust in AI
- Comparative evaluation of contribution‑value plots (JVCA 2021) – Visualizing feature contributions
- A Performance‑Explainability Framework (arXiv 2020) – Benchmarking time series classifiers
- EXPLAN: Explaining Black‑box Classifiers (IEEE 2020) – Adaptive neighborhood generation for explanations
- GRACE: Generating Contrastive Samples (arXiv 2019) – Contrastive explanations for neural networks
- ExplainExplore: Visual Exploration of ML Explanations (TVCG 2020) – Interactive tool for exploring explanations
- FACE: Feasible and Actionable Counterfactual Explanations (AIES 2019) – Actionable counterfactuals for recourse
- Explainability Fact Sheets (arXiv 2019) – Framework to assess explanation methods
- One Explanation Does Not Fit All (ACM 2020) – Interactive explanations for transparency
- FAT Forensics (Python toolbox) – Fairness, accountability, transparency tools
- Adaptive Explainable Neural Networks (AxNNs) (arXiv 2020) – Self‑explaining neural networks
- Information Leakage in Embedding Models (arXiv 2020) – Privacy risks in embeddings
- Closing the AI Accountability Gap (arXiv 2020) – End‑to‑end auditing framework
- Explaining the Explainer: A First Theoretical Analysis of LIME (arXiv 2020) – Theoretical foundations of LIME
- bLIMEy: Surrogate Prediction Explanations Beyond LIME (arXiv 2019) – Improved local surrogate models
- Are Sixteen Heads Really Better than One? (NeurIPS 2019) – Analysis of multi‑head attention
- Revealing the Dark Secrets of BERT (EMNLP 2019) – Probing BERT’s internal representations
- Explanation in AI: Insights from Social Sciences (Synthese 2019) – Social science perspective on XAI
- AnchorViz (Microsoft Research 2019) – Interactive concept discovery
- Randomized Ablation Feature Importance (arXiv 2019) – Model‑agnostic feature importance
- Explainable AI for Trees (Nature MI 2019) – SHAP for tree ensembles
- One Explanation Does Not Fit All: Toolkit & Taxonomy (arXiv 2019) – IBM AIX360 toolkit
- LIRME: Locally Interpretable Ranking Model Explanation (JCDL 2019) – Explanations for ranking models
- Understanding complex predictive models with Ghost Variables (arXiv 2019) – Ghost variables for interpretability
- Unmasking Clever Hans predictors (Nature Comms 2019) – Assessing what models really learn
- Feature Impact for Prediction Explanation (2019) – Feature impact method
- Relative Attributing Propagation (ICLR 2019) – Comparative unit attribution in DNNs
- The Bouncer Problem: Challenges to Remote Explainability (arXiv 2019) – Limits of remote explanations
- Understanding Black‑box Predictions via Influence Functions (ICML 2017) – Influence functions for model debugging
- Towards XAI: Structuring the Processes of Explanations (2019) – Process model for explanations
- Evaluation of AutoML Approaches (arXiv 2019) – Comparison of AutoML tools
- Intelligible Models for Healthcare (KDD 2015) – Risk prediction with intelligible models
- Shapley Decomposition of R‑Squared (arXiv 2019) – R² decomposition using Shapley
- Data Shapley: Equitable Valuation of Data (ICML 2019) – Shapley for data valuation
- Stratified Partial Dependence (arXiv 2019) – PDP for codependent variables
- DLIME: Deterministic Local Interpretable Model‑Agnostic Explanations (arXiv 2019) – Deterministic LIME variant
- Exploiting patterns to explain individual predictions (KAIS 2019) – Pattern‑based explanations
- Fair is Better than Sensational (arXiv 2019) – Bias in word embeddings
- Interpretable Counterfactual Explanations Guided by Prototypes (ECML 2019) – Prototype‑guided counterfactuals
- Learning Explainable Models Using Attribution Priors (ICML 2019) – Priors for attribution
- Guidelines for Responsible and Human‑Centered XAI (arXiv 2019) – Human‑centered XAI guidelines
- Concept Tree for Interpretable Surrogate Trees (arXiv 2019) – High‑level concept trees
- Ten Things You Wish You Had Known Earlier (arXiv 2019) – Practical data analysis tips
- Proposals for Model Vulnerability and Security (O’Reilly 2019) – Security considerations for ML
- On Explainable Machine Learning Misconceptions (2019) – Correcting XAI myths
- Model Cards for Model Reporting (FAccT 2019) – Standardised model reporting
- Unbiased Measurement of Feature Importance in Tree‑Based Methods (arXiv 2019) – Unbiased permutation importance
- Please Stop Permuting Features (arXiv 2019) – Critique of permutation importance
- Why should you trust my interpretation? Uncertainty in LIME (arXiv 2019) – Uncertainty quantification for LIME
- Aequitas: A Bias and Fairness Audit Toolkit (arXiv 2018) – Bias audit toolkit
- Variable Importance Clouds (arXiv 2019) – Variable importance over good models
- No performance benefit of ML over logistic regression (JCE 2019) – Systematic review
- iBreakDown: Uncertainty of Model Explanations (arXiv 2019) – Uncertainty for break‑down attributions
- SIPA (Sampling, Intervention, Prediction, Aggregation) (arXiv 2019) – General framework for model‑agnostic interpretations
- Quantifying Interpretability through Functional Decomposition (arXiv 2019) – Interpretability metric via decomposition
- One pixel attack for fooling DNNs (arXiv 2017) – Minimal perturbation adversarial attack
- VINE: Visualizing Statistical Interactions (arXiv 2019) – Interaction visualization
- Clinical applications of ML: beyond the black box (BMJ 2019) – Clinical XAI perspective
- ICIE 1.0: Interactive Contextual Interaction Explanations (DIONE 2019) – Contextual interaction explanations
- Explanation in Human‑AI Systems: Literature Meta‑Review (arXiv 2019) – Comprehensive XAI literature review
- Explaining Explanations: Overview of Interpretability (AI Magazine 2019) – Tutorial on interpretability
- SAFE ML: Surrogate Assisted Feature Extraction (arXiv 2019) – Feature extraction for model learning
- Attention is not Explanation (NAACL 2019) – Critique of attention as explanation
- Efficient Search for Diverse Coherent Explanations (arXiv 2019) – Diverse counterfactuals
- Seven Myths in Machine Learning Research (arXiv 2019) – Common misconceptions
- Aggregating Weighted Feature Attributions (arXiv 2019) – Aggregation of attributions
- An Evaluation of the Human‑Interpretability of Explanation (arXiv 2019) – Human evaluation of explanations
- Interpretable ML: definitions, methods, applications (arXiv 2019) – Survey of interpretable ML
- Learning Optimal and Fair Decision Trees (AAAI 2019) – Fair decision tree learning
- Contrastive Backpropagation (ICLR 2019) – Contrastive explanations for CNNs
- Conversational Explanations via Class‑contrastive Counterfactuals (IJCAI 2018) – Dialogue‑based explanations
- TCAV: Testing with Concept Activation Vectors (ICML 2018) – Concept‑based explanations
- Machine Decisions and Human Consequences (arXiv 2018) – Ethical implications of ML decisions
- Controversy Rules (arXiv 2018) – Discovering regions of disagreement
- Distill‑and‑Compare: Auditing Black‑Box Models (AIES 2018) – Model distillation for auditing
- DIVE: Mixed‑Initiative Data Exploration (HILDA 2018) – Interactive data exploration
- Learning Explanatory Rules from Noisy Data (JAIR 2018) – Differentiable logic for XAI
- Towards Interpretable R‑CNN (ICCV 2017) – Unfolding latent structures
- Fair lending needs explainable models (ICMLA 2018) – XAI for responsible lending
- ICIE 1.0 (extended) (DIONE 2018) – Interactive contextual explanations
- Delayed Impact of Fair Machine Learning (ICML 2018) – Long‑term effects of fairness interventions
- The Challenge of Crafting Intelligible Intelligence (CACM 2018) – Challenges in XAI
- Globally Consistent Explanations for Credit Risk (NeurIPS 2018) – Interpretable model
- HELOC Risk Evaluation by Topological Decomposition (ICDM 2018) – Topological analysis for risk
- From Black‑Box to White‑Box: Interpretable Learning with Kernel Machines (BENELEARN 2018) – Kernel methods for interpretability
- From Soft Classifiers to Hard Decisions: How fair? (FAT 2019) – Fairness in thresholded classifiers
- Survey of Methods for Explaining Black Box Models (ACM CSUR 2019) – Comprehensive survey
- Deep k‑Nearest Neighbors (ICLR 2018) – Interpretable deep learning with k‑NN
- RISE: Randomized Input Sampling for Explanation (arXiv 2018) – Model‑agnostic explanation by input sampling
- Visualizing Feature Importance for Black Box Models (2018) – Visual feature importance
- Interpreting Blackbox Models via Model Extraction (IJCAI 2017) – Model extraction for interpretation
- Game‑Based Verification of DNNs (arXiv 2018) – Verification via game theory
- All Models are Wrong: Variable Importance for Black‑Box Models (JSAN 2019) – Model class reliance
- Please Stop Explaining Black Box Models for High Stakes Decisions (NeurIPS 2018) – Critical view of XAI for high stakes
- State of the Art in Fair ML (arXiv 2018) – Fairness survey
- Explaining Explanations in AI (CHI 2019) – Meta‑analysis of XAI definitions
- On Human Predictions with Explanations (CogSci 2018) – Human‑AI joint decision making
- On the Art and Science of ML Explanations (2018) – Art and science of XAI
- Interpretable to Whom? Role‑based Model (FAT 2018) – Stakeholder‑centric interpretability
- Interpreting Models by Allowing to Ask (NeurIPS 2018) – Interactive query‑based explanations
- Contrastive Explanation: A Structural‑Model Approach (AAAI 2019) – Structural causal models for contrastive explanations
- Explainable AI for Designers: Human‑Centered Perspective (2018) – XAI for creative design
- AI in Education needs interpretable ML (2018) – XAI for open learner modelling
- Instance‑Level Explanations for Fraud Detection (AAAI 2018) – Case study in fraud
- On the Robustness of Interpretability Methods (NeurIPS 2018) – Robustness of saliency maps
- Contrastive Explanations with Local Foil Trees (FAT 2019) – Foil trees for contrastive explanations
- Evaluating Feature Importance Estimates (arXiv 2018) – Metrics for feature importance
- Interpreting Embedding Models of Knowledge Bases (ACL 2018) – Pedagogical approach to KG embeddings
- Manifold: Model‑Agnostic Framework (arXiv 2018) – Framework for model interpretation and diagnosis
- Interpretable Explanations by Meaningful Perturbation (ICCV 2017) – Perturbation‑based saliency
- Interpretability is Harder in Multiclass (IFIP 2018) – Axioms for multiclass additive models
- Statistical Paradises and Paradoxes in Big Data (Harvard 2015) – Statistical pitfalls in big data
- Explanation Methods in Deep Learning (EB 2018) – Users, values, concerns, challenges
- TED: Teaching AI to Explain its Decisions (AIES 2018) – Teaching AI to explain
- Transparency in Algorithmic and Human Decision‑Making (Philos. Technol. 2019) – Double standard of transparency
- Comparative study of fairness‑enhancing interventions (ICML 2018) – Fairness interventions benchmark
- Check yourself before you wreck yourself (2018) – Predictive simulation for discrete choice
- Example and Feature importance‑based Explanations (2018) – Hybrid explanation method
- Explainable AI: Beware of Inmates Running the Asylum (IJCAI 2017) – Social and behavioural sciences for XAI
- Interactive Graphics for Visually Diagnosing Forest Classifiers (R Journal 2018) – Visual diagnostics for random forests
- Black Hat Visualization (DECISIVe 2017) – Deceptive visualization practices
- Workflow for Visual Diagnostics of Binary Classifiers (DIAGRAM 2017) – Instance‑level explanations for diagnostics
- Fair Forests: Regularized Tree Induction to Minimize Model Bias (FAT 2018) – Fair decision forests
- Towards A Rigorous Science of Interpretable ML (ICML 2017) – Foundational paper for interpretability science
- Attentive Explanations: Justifying Decisions (CVPR 2017) – Attention‑based justifications
- SPINE: SParse Interpretable Neural Embeddings (AAAI 2018) – Sparse embeddings for interpretability
- Detecting concept drift using model explanation (IJCNN 2017) – XAI for drift detection
- Explanation of Prediction Models with ExplainPrediction (Informatica 2017) – Software for model explanation
- What do we need to build XAI systems for the medical domain? (2017) – XAI requirements for medicine
- Equality of Opportunity in Supervised Learning (NeurIPS 2016) – Foundational fairness paper
- Interacting with Predictions: Visual Inspection of Black‑box Models (CHI 2016) – Prospector tool
- The Mythos of Model Interpretability (ICML 2016 workshop) – Influential paper on interpretability as a myth
- What makes classification trees comprehensible? (ESWA 2016) – Comprehension metrics for trees
- Residual‑based Predictiveness Curve (Stat. Med. 2015) – Visual tool for prediction performance
- Interpretable classifiers using rules and Bayesian analysis (Ann. Appl. Stat. 2016) – Stroke prediction model
- How to Explain Individual Classification Decisions (ILP 2009) – Early work on explanation
- The Tyranny of Tacit Knowledge (2005) – Tacit knowledge in AI
- Discovering additive structure in black box functions (KDD 2004) – Additive models for black boxes
- ExplainX – Open‑source XAI platform
- EthicalML/xai – XAI toolkit with documentation
- tf-explain – TensorFlow 2.0 explainability utilities
- InterpretML by Microsoft – Microsoft’s interpretability toolkit
- sklearn_explain – Explain scikit‑learn models
- heatmapping.org – Website for heatmap attribution methods
- iNNvestigate – Neural network analysis toolbox
- ggeffects (R package) – Marginal effects for regression models
- Contrastive LRP – Contrastive explanations via LRP
- Relative Attributing Propagation – RAP implementation
- MAGMIL (Model Agnostic Methods for Interpretable ML) – Collection of XAI methods
- Black Box Auditing (certifying and removing disparity) – Fairness auditing tool
- Skater – Model interpretation library
- Weight Watcher – Spectral analysis for DNNs
- Adversarial Robustness Toolbox (ART) – Security and robustness for ML
- Model Describer – Describe model predictions
- AI Fairness 360 (AIF360) – Fairness metrics and algorithms
- The What‑If Tool – Interactive model probing
- Impact encoding for categorical features – Target encoding implementation
- FairTest – Testing bias in ML pipelines
- Explanation Explorer – Interactive explanation visualization
- AI Black Box Horror Stories (Medium 2019) – Cautionary tales
- Artificial Intelligence Confronts a ‘Reproducibility’ Crisis (Wired 2019) – Reproducibility in AI
- Model explainers and the press secretary (Medium 2019) – Trust optimisation in XAI
- Decoding the Black Box: Interpretable ML in Python (AV 2019) – Tutorial on XAI with Python
- I, Black Box: XAI and Limits of Human Deliberation (WOTR 2019) – Military perspective
- Teaching AI, Ethics, Law and Policy (arXiv 2019) – Syllabus for AI ethics
- An introduction to XAI, and why we need it (KDnuggets 2019) – Introductory article
- The AI Black Box Explanation Problem (ERCIM 2019) – XAI challenges
- VOZIQ Launches ‘Agent Connect,’ an XAI Product (PR 2019) – Commercial XAI for retention
- Derisking machine learning and AI (McKinsey 2019) – Risk management for ML
- Explainable AI should help us avoid a third ‘AI winter’ (Computing 2019) – XAI and AI winters
- Explainable AI: From Prediction To Understanding (Medium 2019) – XAI for understanding
- Why XAI is the future of marketing and e‑commerce (TFC 2019) – Business case for XAI
- Interpretable AI or How I Learned to Stop Worrying and Trust AI (TDS 2019) – Tutorial on interpretability
- In Search of XAI (Geopolitical Monitor 2019) – Geopolitical implications
- XAI and the Rebirth of Rules (Forbes 2019) – XAI and rule‑based systems
- Attacking discrimination with smarter ML (Google Research 2019) – Fairness via regularization
- Tunable and Explainable Recommender Systems (Insight 2019) – Controller for recommender explanations
- Machine Learning is Creating a Crisis in Science (GovernmentCIO 2019) – ML reproducibility crisis
- Artificial Intelligence and Ethics (Harvard Mag 2019) – AI limitations and ethics
- Building Trusted Human‑Machine Partnerships (DARPA 2019) – DARPA XAI program overview
- How Augmented Analytics and XAI Will Cause a Disruption (AI 2019) – Market disruption
- Why XAI is the Next Frontier in Financial Crime Fighting (Banking Exchange 2019) – XAI for AML
- Machine Learning Interpretability: Do You Know What Your Model Is Doing? (Inovex 2019) – Interpretability overview
- Building explainable machine learning models (FastDataScience 2019) – Practical guide
- AI is not IT (LinkedIn 2019) – AI vs traditional IT
- A computer program used for bail and sentencing… (WashPost 2016) – COMPAS debate
- Machine learning ‘causing science crisis’ (BBC 2019) – ML in science
- Automatic Machine Learning is broken (Medium 2019) – Critiques of AutoML
- Charles River Analytics creates tool for AI to communicate (Mil‑Embedded 2019) – XAI for human‑AI comms
- Inside DARPA’s effort to create XAI (BDTechTalks 2019) – DARPA XAI explanation
- Boston University researchers develop framework to improve AI fairness (VentureBeat 2019) – Fairness framework
- Understanding Explainable AI (Quantiply 2018) – Intro to XAI
- Uber Has Open‑Sourced Autonomous Vehicle Visualization (DesignNews 2018) – Uber’s AV visualization
- Holy Grail of AI for Enterprise – XAI (Good Audience 2018) – XAI for business
- Artificial Intelligence Is Not A Technology (Forbes 2018) – AI as philosophy
- The Building Blocks of Interpretability (Distill 2018) – Interpretability primitives
- Why Machine Learning Interpretability Matters (Dataiku 2018) – Business case
- IBM, Harvard develop tool for black box problem in AI translation (VentureBeat 2018) – XAI for translation
- The Five Tribes of Machine Learning Explainers (2018) – Categorisation of XAI methods
- Beware Default Random Forest Importances (Explained.ai 2018) – Biases in RF importance
- A Case For XAI (KDnuggets 2018) – Case for XAI
- Ethics of AI: A data scientist’s perspective (Medium 2018) – Ethical guidelines for DS
- Explainable AI vs Explaining AI (Medium 2018) – Distinction between XAI and explanation
- Regulating Black‑Box Medicine (Michigan Law Review 2018) – Legal perspective on black‑box models
- 3 Signs of a Good AI Model (TDWI 2018) – Practical criteria
- Rapid new advances are now underway in AI (Technative 2018) – XAI movement
- Why We Need to Audit Algorithms (HBR 2018) – Algorithm auditing
- Taking machine thinking out of the black box (MIT News 2018) – MIT Lincoln Lab XAI
- Explainable AI won’t deliver. Here’s why (Hackernoon 2018) – Skeptical view
- We Need an FDA For Algorithms (Nautilus 2018) – Regulatory analogy
- Explainable AI, interactivity and HCI (LinkedIn 2018) – HCI perspective
- Why your firm must embrace XAI (HFS 2018) – Business risk
- Explainable AI : The margins of accountability (Information Age 2018) – Accountability in XAI
- Sent to Prison by a Software Program’s Secret Algorithms (NYT 2017) – Algorithmic sentencing controversy
- AI Could Resurrect a Racist Housing Policy (Vice 2017) – Fair housing and AI
- How We Analyzed the COMPAS Recidivism Algorithm (ProPublica 2016) – Landmark COMPAS investigation
- Shedding Light on Black Box ML Algorithms (MSc thesis 2018) – Axiomatic quality assessment of explanations
- Uncertainty and Label Noise in Machine Learning (PhD thesis 2016) – Thesis on uncertainty and label noise
- Explaining Explainable AI (BrightTalk 2018) – Video webinar
- Approaches to Fairness in ML with Richard Zemel (TWIML 2018) – Podcast on fairness
- Making Algorithms Trustworthy with David Spiegelhalter (TWIML 2018) – Trust in algorithms
- 2nd Workshop on XAI proceedings (2018) – XAI workshop papers
- Explainable AI (BDC 2018) – Barcelona Digital Center talk
- Trust and explainability: Relationship between humans & AI (2018) – Trust in AI
- 21 fairness definitions and their politics (FAT ML 2018) – Tutorial on fairness definitions
- Proceedings of ICML WHI 2018 (arXiv 2018) – Workshop on human interpretability
- NIPS 2017 Tutorial on Fairness in ML – Fairness tutorial
- Interpretability for AI safety (NIPS 2017) – AI safety and interpretability
- Debugging machine‑learning (MILA 2017) – Debugging ML systems
- XAI and algorithmic fairness (GitHub repo) – Collection by Andrey Sharapov
- FAT ML (Fairness, Accountability, Transparency in ML) – Community website
- UW Interactive Data Lab papers – Research papers on interactive data visualization
- CS 294: Fairness in ML (Berkeley) – Course on fairness
- Machine Learning Fairness by Google – Google’s fairness resources
- Awesome Interpretable ML (GitHub) – Curated list by Michał Łopuszyński
- XAI: Expanding the frontiers of AI (LinkedIn Learning) – LinkedIn Learning course
- Google – Explainable AI – Google Cloud XAI documentation
- Google Explainability whitepaper – Google’s whitepaper on AI explainability
- A Better Baseline for AVA (CVPR 2018) – Action recognition baseline
- Real‑Time End‑to‑End Action Detection (arXiv 2018) – Two‑stream networks for action detection
- Human Action Localization with Sparse Spatial Supervision (arXiv 2017) – Weak supervision for action localization
- Unsupervised Action Discovery and Localization (ICCV 2017) – Unsupervised approach
- Spatial‑Aware Object Embeddings for Zero‑Shot Action (ICCV 2017) – Zero‑shot action classification
- Action Tubelet Detector (ICCV 2017) – Spatio‑action detection with tubelets
- T‑CNN for Action Detection (ICCV 2017) – Tube convolutional neural network
- Chained Multi‑stream Networks (ICCV 2017) – Pose, motion, appearance fusion
- TORNADO: Spatio‑Temporal Regression for Action Proposals (ICCV 2017) – Action proposal network
- Online Real time Multiple Spatiotemporal Action Localisation (ICCV 2017) – Online action detection
- AMTnet: Action‑Micro‑Tube regression (ICCV 2017) – Micro‑tube regression
- Am I Done? Predicting Action Progress (BMVC 2017) – Action progress prediction
- Generic Tubelet Proposals for Action Localization (arXiv 2017) – Tubelet proposal generation
- Incremental Tube Construction for Action Detection (arXiv 2017) – Incremental tube building
- Multi‑region two‑stream R‑CNN for action detection (ECCV 2016) – Faster R‑CNN for action detection
- Spot On: Action Localization from Pointly‑Supervised Proposals (ECCV 2016) – Point supervision for action localization
- Deep Learning for Detecting Multiple Space‑Time Action Tubes (BMVC 2016) – Tube detection with deep learning
- Learning to track for spatio‑temporal action localization (ICCV 2015) – Tracking for action localization
- Action detection by implicit intentional motion clustering (ICCV 2015) – Motion clustering approach
- Finding Action Tubes (CVPR 2015) – Action tube generation
- APT: Action localization proposals from dense trajectories (BMVC 2015) – Action proposal from trajectories
- Spatio‑Temporal Object Detection Proposals (ECCV 2014) – 3D proposal generation
- Action localization with tubelets from motion (CVPR 2014) – Motion‑based tubelets
- Spatiotemporal deformable part models for action detection (CVPR 2013) – DPM for action detection
- Action localization in videos through context walk (ICCV 2015) – Context walk method
- Fast Action Proposals for Action Detection (CVPR 2015) – Fast action proposals (FAP)
- Policy Gradient Methods for RL with Function Approximation (NeurIPS 1999) – Foundational policy gradient paper
- Actor‑Critic Algorithms (NeurIPS 2000) – Actor‑critic framework
- Playing Atari with Deep RL (DQN) (Nature 2015) – Deep Q‑Network paper
- Deep Deterministic Policy Gradient (DDPG) (ICLR 2016) – Continuous action RL
- Asynchronous Methods for Deep RL (A3C) (ICML 2016) – Asynchronous actor‑critic
- Trust Region Policy Optimization (TRPO) (ICML 2015) – Trust region optimization
- Proximal Policy Optimization Algorithms (PPO) (arXiv 2017) – Popular policy gradient method
- Bidirectional RNN (TSP 1997) – Schuster & Paliwal
- Multi‑Dimensional RNN (ICANN 2007) – Graves et al.
- Gated Feedback RNN (ICML 2015) – Chung et al.
- Tree‑Structured LSTM (ACL 2015) – Tai et al.
- Grid LSTM (arXiv 2015) – Kalchbrenner et al.
- Segmental RNN (ICLR 2016) – Kong et al.
- Order Matters: Sequence to sequence for sets (ICLR 2016) – Vinyals et al.
- Hierarchical Multiscale RNN (arXiv 2016) – Chung et al.
- LSTM (Neural Computation 1997) – Hochreiter & Schmidhuber
- GRU (EMNLP 2014) – Cho et al.
- Neural Turing Machine (arXiv 2014) – Graves et al.
- Neural GPU (ICML 2016) – Kaiser & Sutskever
- Memory Networks (arXiv 2014) – Weston et al.
- Pointer Networks (NIPS 2015) – Vinyals et al.
- Deep Attention Recurrent Q‑Network (DARQN) (arXiv 2015) – Sorokin et al.
- Dynamic Memory Networks (ICML 2016) – Kumar et al.
- AlphaPose (PyTorch) – Real‑time pose estimation and tracking
- Detect‑and‑Track: Efficient Pose Estimation in Videos (arXiv 2017) – Girdhar et al.
- OpenPose Library (CMU) – Real‑time multi‑person pose estimation
- Realtime Multi‑Person 2D Pose Estimation (Part Affinity Fields) (CVPR 2017) – Cao et al.
- DensePose (CVPR 2018) – Dense human pose estimation
- MultiPoseNet (ECCV 2018) – Kocabas et al.
- DeepLabCut (Nature Neurosc 2018) – Markerless pose estimation
- Actional‑Structural GCN for Skeleton‑Based Action Recognition (CVPR 2019) – Li et al.
- Attention Enhanced Graph Convolutional LSTM (CVPR 2019) – Si et al.
- View Adaptive Neural Networks for Skeleton‑Based Action Recognition (TPAMI 2019) – Zhang et al.
- Spatial Temporal Graph Convolutional Networks (ST‑GCN) (AAAI 2018) – Yan et al.
- Deep Progressive Reinforcement Learning for Skeleton Action (CVPR 2018) – Tang et al.
- Co‑occurrence Feature Learning from Skeleton Data (IJCAI 2018) – Li et al.
- Part‑based Graph Convolutional Network for Action Recognition (BMVC 2018) – Thakkar et al.
- Deep Learning (Nature 2015) – LeCun, Bengio, Hinton – Foundational review
- LSTM: A Search Space Odyssey (arXiv 2015) – Greff et al.
- Critical Review of RNNs for Sequence Learning (arXiv 2015) – Lipton
- Visualizing and Understanding RNNs (arXiv 2015) – Karpathy et al.
- Empirical Exploration of RNN Architectures (ICML 2015) – Jozefowicz et al.
- RNN based Language Model (Interspeech 2010) – Mikolov et al.
- Extensions of RNN Language Model (ICASSP 2011) – Mikolov et al.
- RNN LM in Meeting Recognition (Interspeech 2011) – Kombrink et al.
- Hierarchical Neural Autoencoder for Paragraphs (ACL 2015) – Li et al.
- Skip‑Thought Vectors (NIPS 2015) – Kiros et al.
- Character‑Aware Neural Language Models (AAAI 2016) – Kim et al.
- Tree RNNs for Language Modeling (ACL 2016) – Zhang et al.
- The Goldilocks Principle (ICLR 2016) – Hill et al.
- DNNs for Acoustic Modeling in Speech Recognition (IEEE SPM 2012) – Hinton et al.
- Speech Recognition with Deep RNNs (ICASSP 2013) – Graves et al.
- Attention‑Based Models for Speech Recognition (NIPS 2015) – Chorowski et al.
- Fast and Accurate RNN Acoustic Models (arXiv 2015) – Sak et al.
- Recurrent Continuous Translation Models (EMNLP 2013) – Kalchbrenner & Blunsom
- Learning Phrase Representations (EMNLP 2014) – Cho et al.
- Properties of Neural Machine Translation (SSST‑8 2014) – Cho et al.
- Overcoming Curse of Sentence Length (SSST‑8 2014) – Pouget‑Abadie et al.
- Neural Machine Translation by Jointly Learning to Align (ICLR 2015) – Bahdanau et al.
- On using very large target vocabulary (ACL 2015) – Jean et al.
- On Using Monolingual Corpora in NMT (arXiv 2015) – Gulcehre et al.
- Sequence to Sequence Learning with Neural Networks (NIPS 2014) – Sutskever et al.
- Addressing the Rare Word Problem in NMT (ACL 2015) – Luong et al.
- Deep Memory‑based Architecture for Sequence‑to‑Sequence (arXiv 2015) – Meng et al.
- Effective Approaches to Attention‑based NMT (EMNLP 2015) – Luong et al.
- Multi‑Way, Multilingual NMT (arXiv 2016) – Firat et al.
- Neural Responding Machine for Short‑Text Conversation (ACL 2015) – Shang et al.
- A Neural Conversational Model (arXiv 2015) – Vinyals & Le
- The Ubuntu Dialogue Corpus (arXiv 2015) – Lowe et al.
- Evaluating Prerequisite Qualities for End‑to‑End Dialog (arXiv 2015) – Dodge et al.
- Dialog‑based Language Learning (arXiv 2016) – Weston
- Learning End‑to‑End Goal‑Oriented Dialog (arXiv 2016) – Bordes & Weston
- Towards AI‑Complete QA: Prerequisite Toy Tasks (arXiv 2015) – Weston et al.
- Simple Question Answering with Memory Networks (NAACL 2016) – Bordes et al.
- Teaching Machines to Read and Comprehend (NIPS 2015) – Hermann et al.
- Ask Me Anything: Dynamic Memory Networks for NLP (ICML 2016) – Kumar et al.
- Recurrent Convolutional Neural Networks for Scene Labeling (ICML 2014) – Pinheiro & Collobert
- Recurrent CNN for Object Recognition (CVPR 2015) – Liang & Hu
- Scene Labeling with LSTM (CVPR 2015) – Byeon et al.
- Recurrent CNNs for Object‑Class Segmentation of RGB‑D Video (IJCNN 2015) – Pavel et al.
- Conditional Random Fields as RNNs (ICCV 2015) – Zheng et al.
- Semantic Object Parsing with Local‑Global LSTM (arXiv 2015) – Liang et al.
- Inside‑Outside Net (IOU) for Object Detection (CVPR 2016) – Bell et al.
- First Step toward Model‑Free, Anonymous Object Tracking (arXiv 2015) – Gan et al.
- DRAW: Recurrent Neural Network for Image Generation (ICML 2015) – Gregor et al.
- Unveiling the Dreams of Word Embeddings: Language‑Driven Image Generation (arXiv 2015) – Lazaridou et al.
- Generative Image Modeling Using Spatial LSTMs (NIPS 2015) – Theis & Bethge
- Pixel RNN (ICML 2016) – van den Oord et al.
- Unsupervised Learning of Video Representations using LSTMs (ICML 2015) – Srivastava et al.
- Spatio‑temporal video autoencoder with differentiable memory (arXiv 2015) – Patraucean et al.
- Explain Images with Multimodal RNN (m‑RNN) (arXiv 2014) – Mao et al.
- Deep Captioning with m‑RNN (ICLR 2015) – Mao et al.
- Unifying Visual‑Semantic Embeddings with Multimodal Neural Language Models (TACL 2015) – Kiros et al.
- Long‑term Recurrent Convolutional Networks (LRCN) (CVPR 2015) – Donahue et al.
- Show and Tell: Neural Image Caption Generator (CVPR 2015) – Vinyals et al.
- Deep Visual‑Semantic Alignments (CVPR 2015) – Karpathy & Fei‑Fei
- From Captions to Visual Concepts and Back (CVPR 2015) – Fang et al.
- Learning a Recurrent Visual Representation (CVPR 2015) – Chen & Zitnick
- Show, Attend, and Tell (ICML 2015) – Xu et al.
- Phrase‑based Image Captioning (ICML 2015) – Lebret et al.
- Learning like a Child: Fast Novel Visual Concept Learning (arXiv 2015) – Mao et al.
- Exploring Nearest Neighbor Approaches for Image Captioning (arXiv 2015) – Devlin et al.
- Language Models for Image Captioning (arXiv 2015) – Devlin et al.
- Image Captioning with Intermediate Attributes Layer (arXiv 2015) – Wu et al.
- Learning language through pictures (arXiv 2015) – Chrupala et al.
- Describing Multimedia Content with Attention (arXiv 2015) – Cho et al.
- Image Representations and New Domains in Neural Image Captioning (arXiv 2015) – Hessel et al.
- Translating Videos to Natural Language (arXiv 2014) – Venugopalan et al.
- Joint Modeling Embedding and Translation for Video and Language (arXiv 2015) – Pan et al.
- Sequence to Sequence – Video to Text (arXiv 2015) – Venugopalan et al.
- Describing Videos by Exploiting Temporal Structure (arXiv 2015) – Yao et al.
- The Long‑Short Story of Movie Description (arXiv 2015) – Rohrbach et al.
- Aligning Books and Movies (arXiv 2015) – Zhu et al.
- Hierarchical Recurrent Neural Encoder for Video Captioning (arXiv 2015) – Pan et al.
- Empirical performance upper bounds for image and video captioning (arXiv 2015) – Yao et al.
- VQA: Visual Question Answering (ICCV 2015) – Antol et al.
- Ask Your Neurons: A Neural‑based Approach to Visual QA (ICCV 2015) – Malinowski et al.
- Exploring Models and Data for Image QA (ICML 2015 workshop) – Ren et al.
- Are You Talking to a Machine? Multilingual Image QA (NIPS 2015) – Gao et al.
- Multimodal Residual Learning for Visual QA (arXiv 2016) – Kim et al.
- Multimodal Compact Bilinear Pooling for VQA (CVPR 2016) – Fukui et al.
- Training Recurrent Answering Units for VQA (arXiv 2016) – Noh & Han
- Hadamard Product for Low‑rank Bilinear Pooling (ICLR 2017) – Kim et al.
- Uncovering Temporal Context for Video Question Answering (MM 2016) – Zhu et al.
- MovieQA: Understanding Stories in Movies (arXiv 2015) – Tapaswi et al.
- Neural Turing Machines (NTM) (arXiv 2014) – Graves et al.
- Memory Networks (ICLR 2015) – Weston et al.
- Inferring Algorithmic Patterns with Stack‑Augmented RNNs (NIPS 2015) – Joulin & Mikolov
- End‑To‑End Memory Networks (NIPS 2015) – Sukhbaatar et al.
- Reinforcement Learning Neural Turing Machines (arXiv 2015) – Zaremba & Sutskever
- RNNs with External Memory for Language Understanding (arXiv 2015) – Peng & Yao
- Neural Programmer (ICLR 2016) – Neelakantan et al.
- Neural Programmer‑Interpreters (ICLR 2016) – Reed & de Freitas
- Neural Random‑Access Machines (ICLR 2016) – Kurach et al.
- Neural GPUs Learn Algorithms (ICLR 2016) – Kaiser & Sutskever
- Skip‑Thought Memory Networks (2015) – Caballero
- Learning Simple Algorithms from Examples (ICLR 2016) – Zaremba et al.
- Listen, Attend, and Walk (arXiv 2015) – Mei et al.
- Policy Learning with Continuous Memory States for Robotic Control (ICML 2015) – Zhang et al.
- Generating Sequences with RNNs (arXiv 2013) – Graves
- Recurrent Models of Visual Attention (NIPS 2014) – Mnih et al.
- Learning to Execute (ICLR 2015) – Zaremba & Sutskever
- Scheduled Sampling for Sequence Prediction (NIPS 2015) – Bengio et al.
- DAG‑Recurrent Neural Networks for Scene Labeling (arXiv 2015) – Shuai et al.
- Recurrent Spatial Transformer Networks (ICLR 2016) – Sonderby et al.
- Batch Normalized RNNs (arXiv 2015) – Laurent et al.
- Deeply‑Recursive Convolutional Network for Image Super‑Resolution (CVPR 2016) – Kim et al.
- ReSeg: Recurrent Neural Network for Object Segmentation (CVPR 2016) – Visin et al.
- On Learning to Think (arXiv 2015) – Schmidhuber
- Tensorflow‑Project‑Template – Template for TF projects
- Domain Transfer Network – Unsupervised cross‑domain image generation
- Show, Attend and Tell – Attention‑based image captioning
- Neural Style (cysmith) – Neural style transfer in TF
- SRGAN (TensorLayer) – Super‑resolution GAN
- Pretty Tensor – High‑level TensorFlow builder API
- Neural Style (anishathalye) – Another neural style implementation
- AlexNet3D – 3D convolutional AlexNet
- TensorFlow White Paper Notes – Annotated notes
- NeuralArt – Neural style implementation
- Generative Handwriting Demo (write‑rnn) – Handwriting generation from Graves paper
- NTM TensorFlow – Neural Turing Machine in TF
- GoogleNet Scene Grouping (thingscoop) – Video scene classification
- Shakespeare translation – Translating between modern English and Shakespeare
- DeepQA Chatbot – Neural conversational model
- Seq2seq‑Chatbot (TensorLayer) – Chatbot in 200 lines
- DCGAN (TensorLayer) – Deep convolutional GAN
- GAN‑CLS (text‑to‑image) – Text to image synthesis
- Unsup‑Im2Im – Unsupervised image translation
- Improved CycleGAN (TensorLayer) – Unpaired image translation
- DAGAN (MRI reconstruction) – Compressed sensing MRI
- Colornet – Image colorization
- Neural Caption Generator (show_attend_and_tell.tf) – Show and Tell implementation
- Weakly_detector (CAM) – Weakly supervised localization
- Dynamic Capacity Networks – DCN implementation
- HMM in TF – HMM algorithms in TensorFlow
- DeepOSM – Train neural nets with OpenStreetMap + satellite imagery
- DQN‑tensorflow (Devsisters) – DQN with OpenAI Gym
- Policy Gradient – Pong (TensorLayer) – Policy gradient for Atari Pong
- Deep Q‑Network – FrozenLake (TensorLayer) – DQN for FrozenLake
- Actor‑Critic – Cartpole (TensorLayer) – AC for Cartpole
- A3C – Bipedal Walker (TensorLayer) – A3C for continuous control
- DAGGER – Torcs – Imitation learning for driving
- TRPO (RL toolbox) – TRPO for continuous/discrete actions
- Highway Network (TF) – Highway networks in TensorFlow
- Hierarchical Attention Networks (HAN) – HAN for document classification
- CNN for Sentence Classification (WildML) – Text CNN in TensorFlow
- End‑To‑End Memory Networks (memn2n) – MemN2N implementation
- Character‑Aware Neural LM (LSTM‑Char‑CNN) – Character‑aware LM
- YOLO TensorFlow ++ – YOLO implementation for mobile
- WaveNet (TF) – WaveNet implementation
- Mnemonic Descent Method (MDM) for face alignment – Recurrent process for face alignment
- CNN visualization (tf_cnnvis) – Visualizing CNNs
- VGAN Tensorflow (video generation) – Generating videos with scene dynamics
- 3D CNN for speaker recognition – 3D CNNs for speaker verification
- U‑Net for brain tumor segmentation – U‑Net implementation
- Spatial Transformer Networks – STN in TensorFlow
- Lip Reading with 3D Architectures – Audio‑visual lip reading
- Hierarchical Attentive Recurrent Tracking (HART) – Attentive object tracking
- Holographic Embeddings (TransX) – Knowledge graph embeddings
- Attend, Infer, Repeat (unsupervised object counting) – Generative model for object counting
- fastText embeddings classifier – FastText implementation
- MusicGenreClassification – Sound classification
- TensorFlow on Kubernetes – Kubernetes TF integration
- Computer Vision Models (tensorflow/models) – Pre‑trained image models
- Ladder Network (semi‑supervised) – Ladder network in Keras/TF
- TF‑Unet (Keras) – U‑Net in Keras
- Sarus TF2 Models (generative) – Autoencoders, VAE, VQ‑VAE, PixelCNN, etc.
- TensorFlow Model Maker – Transfer learning for TF Lite
- Titanic Survival Prediction (Kaggle) – Classic beginner competition
- Iris Flower Classification (Kaggle) – Simple classification
- House Price Prediction (Kaggle) – Regression challenge
- Movie Recommendation System (Kaggle) – Recommender engine
- Sentiment Analysis on Twitter (Kaggle) – NLP sentiment
- Image Classification with CNNs (Digit Recognizer) – MNIST on Kaggle
- Loan Default Prediction (Kaggle) – Credit risk modelling
- Time Series Forecasting with ARIMA (Kaggle) – Web traffic forecasting
- GANs for Image Generation (Generative Dog Images) – GAN challenge
- End‑to‑End MLOps Pipeline (MLflow) – MLOps framework
- Face Recognition System (Kaggle dataset) – Face mask detection dataset
- Build Your Own GPT Model (YouTube) – Tutorial by Andrej Karpathy
- The AI Engineer (newsletter) – Newsletter for AI engineers
- Artificial Intelligence Made Simple (Substack) – AI newsletter
- Secure and Private AI Course (Udacity) – Udacity course on privacy and security in AI
- Notebooks for Secure and Private AI (GitHub) – Course notebooks
- Advanced PySyft tutorials – PySyft tutorials
- Advanced PyGrid examples – PyGrid examples
- Private Deep Learning in TensorFlow Using Secure Computation (arXiv 2018) – Secure computation for TF
- SecureNN (EPRINT 2018) – Efficient and private NN training
- Gazelle (arXiv 2018) – Low latency secure inference
- Chameleon (EPRINT 2017) – Hybrid secure computation for ML
- CryptoDL (arXiv 2017) – DNNs over encrypted data
- MiniONN (CCS 2017) – Oblivious NN predictions
- DeepSecure (arXiv 2017) – Scalable provably‑secure deep learning
- SecureML (EPRINT 2017) – Privacy‑preserving ML system
- CryptoNets (Microsoft 2016) – Neural nets on encrypted data
- Privacy‑Preserving Deep Learning (CCS 2015) – Foundational privacy‑preserving DL paper
Misc