✨ It is a curated collection of remarkable and inspirational material on artificial intelligence, machine learning, and other topics related to the appliance of intelligent approaches to robotics, cyber-physical systems, cloud computing, microservices, and software design. I've personally read most of the materials and had a chance to work with source code examples for my research and learning purposes. I constantly update this list whenever interesting material appears.
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🙌 This collection was created by Oleh Chaplia and is constantly updated.
- Archetype AI
- Artificial Intelligence Research Lab
- Cohere For AI
- Cortical Labs - Dishbrain Intelligence
- Figure.AI
- FinalSpark
- Google AI Lab
- Google DeepMind Research
- IBM Research
- Intel AI Lab
- Machine Intelligence Research Institute
- Machine Learning | Research at Apple
- Meta Research
- Microsoft Research
- NASA - Autonomous Systems & Robotics
- NASA - Intelligent Systems Division
- NASA - Robust Software Engineering
- Nvidia Resources
- OpenAI
- Silo AI: Europe's largest private AI lab
- Berkeley Artificial Intelligence Research
- Machine Learning Research Group, University of Oxford
- MIT-IBM Watson Research Lab
- MIT Computer Science & Artificial Intelligence Laboratory
- The Alan Turing Institute
- Stanford AI Lab
- AI Startup Strategy: A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit
- AI Engineering
- Agile Artificial Intelligence in Pharo: Implementing Neural Networks, Genetic Algorithms, and Neuroevolution
- Apps with GPT-4 and ChatGPT
- Applied Machine Learning and AI for Engineers
- Applied Machine Learning Explainability Techniques
- Artificial Intelligence - Ethical, social, and security impacts for the present and the future
- Artificial Intelligence Illuminated / Ben Coppin
- Artificial Intelligence: A Modern Approach. Third Edition / Stuart Russell & Peter Norvig
- Artificial Intelligence: Foundations of Computational Agents, 3rd Edition
- Bio-Inspired Artificial Intelligence. Theories, Methods, and Technologies / Dario Floreano & Claudio Mattiussi
- Competing in the Age of AI
- Deep Blueberry Book
- Deep Learning
- Designing Autonomous AI
- Distributed Machine Learning Patterns / Yuan Tang
- Dive into Deep Learning
- Effective Machine Learning Teams
- Essential Math for AI
- Generative AI for Software Development
- Grokking Artificial Intelligence Algorithms
- How Machine Learning Works
- Inside Deep Learning
- Intelligent Systems: Architecture, Design, and Control / Alexander M. Meystel, James S. Albus
- Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers using TensorFlow
- Machine Learning Model Serving Patterns and Best Practices
- Math and Architectures of Deep Learning / Krishnendu Chaudhury
- Networking Vehicles to Everything
- Neural Networks and Deep Learning
- Practical Machine Learning for Computer Vision
- Privacy-Preserving Machine Learning
- Prompt Engineering for LLMs
- Quantum Machine Learning: An Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry
- The AI Revolution in Medicine: GPT-4 and Beyond
- The Machine Learning Solutions Architect Handbook - Second Edition
- What Is LLMOps?
- The Software-Defined Vehicle
- Autonomous Mobile Robots and Multi-Robot Systems
- Bio-Inspired Computing and Networking
- Deep Learning for Unmanned Systems
- Genetic Algorithms and Machine Learning for Programmers
- GPU-based Parallel Implementation of Swarm Intelligence Algorithms
- Nature-Inspired Optimization Algorithms
- Swarm Intelligence
- Swarm Intelligence
- Swarm Intelligence Algorithms
- Swarm Intelligence Algorithms (Two Volume Set)
- Swarm Intelligence and Bio-Inspired Computation
- MDPI - AI
- MDPI - Big Data and Cognitive Computing
- MDPI - Computers
- MDPI - Data
- MDPI - Drones
- MDPI - Future Internet
- MDPI - Informatics
- MDPI - Information
- MDPI - Robotics
- Springer - Applied Intelligence
- Springer - Artificial Intelligence Review
- Springer - Autonomous Agents and Multi-Agent Systems
- Springer - Computational and Mathematical Organization Theory
- Springer - Discover Artificial Intelligence
- Springer - Evolutionary Intelligence
- Springer - Journal of Automated Reasoning
- Springer - Journal of Intelligent Information Systems
- Springer - Minds and Machines
- Springer - New Generation Computing
- Springer - Progress in Artificial Intelligence
- ScienceDirect - AI Open
- ScienceDirect - Array
- ScienceDirect - Cognitive Robotics
- ScienceDirect - Future Computing and Informatics Journal
- ScienceDirect - High-Confidence Computing Journal
- ScienceDirect - Intelligent Systems with Applications
- ScienceDirect - International Journal of Cognitive Computing in Engineering
- ScienceDirect - International Journal of Intelligent Networks
- ScienceDirect - Internet of Things and Cyber-Physical Systems
- ScienceDirect - Journal of Automation and Intelligence
- ScienceDirect - Journal of Information and Intelligence
- ScienceDirect - Machine Learning with Applications
- ScienceDirect - SoftwareX
- ScienceDirect - Systems and Soft Computing
- ScienceDirect - Virtual Reality & Intelligent Hardware
- Build a Large Language Model (From Scratch) / Sebastian Raschka
- Generative AI on AWS / Chris Fregly, Antje Barth, Shelbee Eigenbrode
- Large Language Models at Work - Enhancing Software Systems with Language Models / Vlad Rișcuția
- Natural Language Processing with Transformers
- Practical Natural Language Processing
- Speech and Language Processing (3rd ed. draft) / Dan Jurafsky and James H. Martin
- Understanding Large Language Models / Thimira Amaratunga
- A Comprehensive Overview of Large Language Models
- A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks
- Attention Is All You Need
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Automated Design of Agentic Systems
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Beyond Text: A Deep Dive into Large Language Models’ Ability on Understanding Graph Data
- Cramming: Training a Language Model on a Single GPU in One Day
- Discovering Latent Knowledge in Language Models Without Supervision
- Efficient Memory Management for Large Language Model Serving with PagedAttention
- Generative Agents: Interactive Simulacra of Human Behavior
- Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions
- GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables
- MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
- LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference
- LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code Generation
- ReAct: Synergizing Reasoning and Acting in Language Models
- Representation Deficiency in Masked Language Modeling
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
- StarCoder: may the source be with you!
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- Training Compute-Optimal Large Language Models
- An Overview of Large Language Models (LLMs)
- Introducing deep learning and long-short term memory networks / IBM
- Making Peace with LLM Non-determinism
- Non-determinism in GPT-4 is caused by Sparse MoE
- Sampling for Text Generation
- The Annotated Transformer
- What are recurrent neural networks? / IBM
- A Recipe for Training Neural Networks / Andrej Karpathy
- Neural Networks: Zero to Hero / Andrej Karpathy
- Autoregressive Models in Deep Learning — A Brief Survey
- Cookbook — Bayesian Modelling with PyMC3
- Decaying Evidence and Contextual Bandits — Bayesian Reinforcement Learning (Part 2)
- Floating-Point Formats and Deep Learning
- Fruit Loops and Learning - The LUPI Paradigm and SVM+
- Linear Discriminant Analysis for Starters
- Modern Computational Methods for Bayesian Inference — A Reading List
- Multi-Armed Bandits and Conjugate Models — Bayesian Reinforcement Learning (Part 1)
- Portfolio Risk Analytics and Performance Attribution with Pyfolio
- Probabilistic and Bayesian Matrix Factorizations for Text Clustering
- Transformers in Natural Language Processing — A Brief Survey
- Understanding Hate Speech on Reddit through Text Clustering
- What I Wish Someone Had Told Me About Tensor Computation Libraries
- Why Latent Dirichlet Allocation Sucks
- A Visual And Interactive Look at Basic Neural Network Math
- A Visual Intro to NumPy and Data Representation
- Finding the Words to Say: Hidden State Visualizations for Language Models
- How GPT3 Works - Visualizations and Animations
- Interfaces for Explaining Transformer Language Models
- The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
- The Illustrated Retrieval Transformer
- The Illustrated Transformer
- The Illustrated Word2vec
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
- Visualizing Pandas' Pivoting and Reshaping Functions
- Understanding Convolutions on Graphs
- A Gentle Introduction to Graph Neural Networks
- Multimodal Neurons in Artificial Neural Networks
- Understanding RL Vision
- Communicating with Interactive Articles
- Thread: Differentiable Self-organizing Systems
- Exploring Bayesian Optimization
- Visualizing Neural Networks with the Grand Tour
- Thread: Circuits
- Visualizing the Impact of Feature Attribution Baselines
- Computing Receptive Fields of Convolutional Neural Networks
- The Paths Perspective on Value Learning
- A Discussion of Adversarial Examples Are Not Bugs, They Are Features
- Open Questions about Generative Adversarial Networks
- A Visual Exploration of Gaussian Processes
- Visualizing memorization in RNNs
- Exploring Neural Networks with Activation Atlases
- AI Safety Needs Social Scientists
- Differentiable Image Parameterizations
- Feature-wise transformations
- The Building Blocks of Interpretability
- Using Artificial Intelligence to Augment Human Intelligence
- Sequence Modeling With CTC
- Feature Visualization
- Why Momentum Really Works
- Four Experiments in Handwriting with a Neural Network
- Deconvolution and Checkerboard Artifacts
- How to Use t-SNE Effectively
- Attention and Augmented Recurrent Neural Networks
- Attention Viz
- BertViz
- LLM Visualization
- Netron
- Neural Network Visualization
- TensorBoard: TensorFlow's visualization toolkit
- TensorFlow Playground
- TensorFlow Embedding Projector
- Open sourcing the Embedding Projector: a tool for visualizing high dimensional data
- Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
- Artificial Intelligence Programming with Python
- Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python
- Building LLM Powered Applications
- Building LLMs for Production
- Building Recommendation Systems in Python and JAX
- Conversational AI
- Deep Learning for Natural Language Processing
- Deep Learning for Time Series Cookbook
- Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras
- Deep Learning with PyTorch / Eli Stevens, Luca Antiga, and Thomas Viehmann
- Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models
- Explainable AI for Practitioners
- Foundations of Deep Reinforcement Learning: Theory and Practice in Python
- Generative Deep Learning, 2nd Edition
- Hands-On Genetic Algorithms with Python - Second Edition
- Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch
- Inside Deep Learning
- Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
- Low-Code AI
- Machine Learning Approach for Cloud Data Analytics in IoT
- Machine Learning Engineering in Action
- Machine Learning with Python Cookbook, 2nd Edition
- Natural Language Processing with Transformers, Revised Edition
- Practical Deep Learning at Scale with MLflow
- Practical Natural Language Processing
- Programming Large Language Models with Azure Open AI: Conversational programming and prompt engineering with LLMs
- WWDC24: Bring your machine learning and AI models to Apple silicon | Apple
- WWDC24: Deploy machine learning and AI models on-device with Core ML | Apple
- WWDC24: Explore machine learning on Apple platforms | Apple
- WWDC24: Support real-time ML inference on the CPU | Apple
- WWDC24: Train your machine learning and AI models on Apple GPUs | Apple
- WWDC24: What’s new in Create ML | Apple