{"id":28258532,"url":"https://github.com/olehxch/ai","last_synced_at":"2026-02-18T03:03:03.762Z","repository":{"id":248679931,"uuid":"813570196","full_name":"olehxch/ai","owner":"olehxch","description":"🧠 Welcome to the AI and ML resources repository! This curated collection offers books, papers, videos, and practical examples to enhance your AI knowledge. Explore inspiring materials on AI, robotics, cyber-physical systems, cloud computing, microservices, and software design.","archived":false,"fork":false,"pushed_at":"2024-10-24T20:53:05.000Z","size":304,"stargazers_count":7,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-14T07:33:00.464Z","etag":null,"topics":["ai","artificial-intelligence","cloud-computing","cyber-physical-systems","intelligent-systems","llm","machine-learning","microservices","robotics","software-design"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/olehxch.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-06-11T10:24:08.000Z","updated_at":"2025-08-15T18:57:48.000Z","dependencies_parsed_at":"2024-07-16T13:19:42.037Z","dependency_job_id":"5658f78e-33fa-4784-b3b1-71e6d75acc56","html_url":"https://github.com/olehxch/ai","commit_stats":null,"previous_names":["olehxch/ai"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/olehxch/ai","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/olehxch%2Fai","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/olehxch%2Fai/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/olehxch%2Fai/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/olehxch%2Fai/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/olehxch","download_url":"https://codeload.github.com/olehxch/ai/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/olehxch%2Fai/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29566656,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-18T00:47:08.760Z","status":"online","status_checked_at":"2026-02-18T02:00:09.468Z","response_time":162,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","artificial-intelligence","cloud-computing","cyber-physical-systems","intelligent-systems","llm","machine-learning","microservices","robotics","software-design"],"created_at":"2025-05-20T01:14:58.169Z","updated_at":"2026-02-18T03:03:02.144Z","avatar_url":"https://github.com/olehxch.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧬 Artificial Intelligence - Science, Research \u0026 Engineering\n\n✨ 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.\nI'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.\n\n⚡️ You can subscribe to my Medium account to read articles about artificial intelligence, cloud computing, state-of-the-art technologies, and also audio engineering! Here is a link:\n\n[My Articles on Medium](https://medium.com/@olehch)\n\n🙌 This collection was created by Oleh Chaplia and is constantly updated.\n\n## 👨‍🔬 Commercial AI Research Labs\n- [Archetype AI](https://www.archetypeai.io)\n- [Artificial Intelligence Research Lab](https://www.bell-labs.com/research-innovation/projects-and-initiatives/air-lab/)\n- [Cohere For AI](https://cohere.com/research)\n- [Cortical Labs - Dishbrain Intelligence](https://corticallabs.com)\n- [Figure.AI](https://www.figure.ai)\n- [FinalSpark](https://finalspark.com)\n- [Google AI Lab](https://labs.google)\n- [Google DeepMind Research](https://deepmind.com/research/)\n- [IBM Research](https://research.ibm.com)\n- [Intel AI Lab](https://www.intel.com/content/www/us/en/research/ai.html)\n- [Machine Intelligence Research Institute](https://intelligence.org/research-guide/)\n- [Machine Learning | Research at Apple](https://machinelearning.apple.com)\n- [Meta Research](https://research.facebook.com)\n- [Microsoft Research](https://www.microsoft.com/en-us/research/)\n- [NASA - Autonomous Systems \u0026 Robotics](https://www.nasa.gov/intelligent-systems-division/autonomous-systems-and-robotics/)\n- [NASA - Intelligent Systems Division](https://www.nasa.gov/intelligent-systems-division/)\n- [NASA - Robust Software Engineering](https://www.nasa.gov/intelligent-systems-division/robust-software-engineering/)\n- [Nvidia Resources](https://developer.nvidia.com)\n- [OpenAI](https://openai.com/about/)\n- [Silo AI: Europe's largest private AI lab](https://www.silo.ai)\n\n## 👨‍🔬 Academic AI Research Labs\n- [Berkeley Artificial Intelligence Research](https://bair.berkeley.edu)\n- [Machine Learning Research Group, University of Oxford](https://www.robots.ox.ac.uk/~parg/)\n- [MIT-IBM Watson Research Lab](https://mitibmwatsonailab.mit.edu)\n- [MIT Computer Science \u0026 Artificial Intelligence Laboratory](https://www.csail.mit.edu)\n- [The Alan Turing Institute](https://www.turing.ac.uk)\n- [Stanford AI Lab](https://ai.stanford.edu)\n\n## 🤖 Artificial Intelligence\n\n### Books on AI\n- [AI Startup Strategy: A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit](https://www.oreilly.com/library/view/the-ai-revolution/9780138293703/)\n- [AI Engineering](https://www.oreilly.com/library/view/ai-engineering/9781098166298/)\n- [Agile Artificial Intelligence in Pharo: Implementing Neural Networks, Genetic Algorithms, and Neuroevolution](https://www.oreilly.com/library/view/agile-artificial-intelligence/9781484253847/?_gl=1*1q3ra89*_ga*MjAzNjIyODkxLjE2OTUwOTc5NDQ.*_ga_092EL089CH*MTY5NTU5MjIyMy4zLjEuMTY5NTU5NDExMi42MC4wLjA)\n- [Apps with GPT-4 and ChatGPT](https://appswithgpt.com)\n- [Applied Machine Learning and AI for Engineers](https://www.oreilly.com/library/view/applied-machine-learning/9781492098041/)\n- [Applied Machine Learning Explainability Techniques](https://www.oreilly.com/library/view/applied-machine-learning/9781803246154/)\n- [Artificial Intelligence - Ethical, social, and security impacts for the present and the future](https://www.oreilly.com/library/view/artificial-intelligence/9781787783720/)\n- [Artificial Intelligence Illuminated / Ben Coppin](http://futuresoft.yolasite.com/resources/Artificial%20Intelligence%20Illuminated.pdf)\n- [Artificial Intelligence: A Modern Approach. Third Edition / Stuart Russell \u0026 Peter Norvig](https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf)\n- [Artificial Intelligence: Foundations of Computational Agents, 3rd Edition](https://artint.info/3e/html/ArtInt3e.html)\n- [Bio-Inspired Artificial Intelligence. Theories, Methods, and Technologies / Dario Floreano \u0026 Claudio Mattiussi](https://mitpress.mit.edu/9780262547734/bio-inspired-artificial-intelligence/)\n- [Competing in the Age of AI](https://www.oreilly.com/library/view/competing-in-the/9781633697638/)\n- [Deep Blueberry Book](https://mithi.github.io/deep-blueberry/)\n- [Deep Learning](https://www.deeplearningbook.org)\n- [Designing Autonomous AI](https://www.oreilly.com/library/view/designing-autonomous-ai/9781098110741/)\n- [Distributed Machine Learning Patterns / Yuan Tang](https://www.manning.com/books/distributed-machine-learning-patterns)\n- [Dive into Deep Learning](https://d2l.ai)\n- [Effective Machine Learning Teams](https://www.oreilly.com/library/view/effective-machine-learning/9781098144623/)\n- [Essential Math for AI](https://www.oreilly.com/library/view/essential-math-for/9781098107628/)\n- [Generative AI for Software Development](https://www.oreilly.com/library/view/generative-ai-for/9781098162269/)\n- [Grokking Artificial Intelligence Algorithms](https://www.oreilly.com/library/view/grokking-artificial-intelligence/9781617296185/?_gl=1*1q3ra89*_ga*MjAzNjIyODkxLjE2OTUwOTc5NDQ.*_ga_092EL089CH*MTY5NTU5MjIyMy4zLjEuMTY5NTU5NDExMi42MC4wLjA)\n- [How Machine Learning Works](https://livebook.manning.com/book/how-machine-learning-works/welcome/v-5)\n- [Inside Deep Learning](https://www.oreilly.com/library/view/inside-deep-learning/9781617298639/)\n- [Intelligent Systems: Architecture, Design, and Control / Alexander M. Meystel, James S. Albus](https://www.wiley.com/en-us/Intelligent+Systems%3A+Architecture%2C+Design%2C+and+Control-p-9780471193746)\n- [Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers using TensorFlow](https://www.oreilly.com/library/view/learning-deep-learning/9780137470198/)\n- [Machine Learning Model Serving Patterns and Best Practices](https://www.oreilly.com/library/view/machine-learning-model/9781803249902/)\n- [Math and Architectures of Deep Learning / Krishnendu Chaudhury](https://www.manning.com/books/math-and-architectures-of-deep-learning)\n- [Networking Vehicles to Everything](https://www.oreilly.com/library/view/networking-vehicles-to/9781501507205/)\n- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n- [Practical Machine Learning for Computer Vision](https://www.oreilly.com/library/view/practical-machine-learning/9781098102357/)\n- [Privacy-Preserving Machine Learning](https://www.oreilly.com/library/view/privacy-preserving-machine-learning/9781800564671/)\n- [Prompt Engineering for LLMs](https://www.oreilly.com/library/view/prompt-engineering-for/9781098156145/)\n- [Quantum Machine Learning: An Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry](https://www.oreilly.com/library/view/quantum-machine-learning/9781484270981/)\n- [The AI Revolution in Medicine: GPT-4 and Beyond](https://www.oreilly.com/library/view/the-ai-revolution/9780138200145/)\n- [The Machine Learning Solutions Architect Handbook - Second Edition](https://www.oreilly.com/library/view/the-machine-learning/9781805122500/)\n- [What Is LLMOps?](https://www.oreilly.com/library/view/what-is-llmops/9781098154301/)\n- [The Software-Defined Vehicle](https://www.oreilly.com/library/view/the-software-defined-vehicle/9781098157814/)\n\n\n### Swarm Intelligence\n\n- [Autonomous Mobile Robots and Multi-Robot Systems](https://www.oreilly.com/library/view/autonomous-mobile-robots/9781119212867/)\n- [Bio-Inspired Computing and Networking](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)\n- [Deep Learning for Unmanned Systems](https://link.springer.com/book/10.1007/978-3-030-77939-9)\n- [Genetic Algorithms and Machine Learning for Programmers](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)\n- [GPU-based Parallel Implementation of Swarm Intelligence Algorithms](https://www.oreilly.com/library/view/gpu-based-parallel-implementation/9780128093641/)\n- [Nature-Inspired Optimization Algorithms](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)\n- [Swarm Intelligence](https://www.oreilly.com/library/view/swarm-intelligence/9781119865063/)\n- [Swarm Intelligence](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)\n- [Swarm Intelligence Algorithms](https://www.oreilly.com/library/view/swarm-intelligence-algorithms/9780429749506/)\n- [Swarm Intelligence Algorithms (Two Volume Set)](https://www.oreilly.com/library/view/swarm-intelligence-algorithms/9781000168747/)\n- [Swarm Intelligence and Bio-Inspired Computation](https://www.oreilly.com/library/view/swarm-intelligence-and/9780124051638/)\n\n### Open Journals on AI\n\n- [MDPI - AI](https://www.mdpi.com/journal/ai)\n- [MDPI - Big Data and Cognitive Computing](https://www.mdpi.com/journal/BDCC)\n- [MDPI - Computers](https://www.mdpi.com/journal/computers)\n- [MDPI - Data](https://www.mdpi.com/journal/data)\n- [MDPI - Drones](https://www.mdpi.com/journal/drones)\n- [MDPI - Future Internet](https://www.mdpi.com/journal/futureinternet)\n- [MDPI - Informatics](https://www.mdpi.com/journal/informatics)\n- [MDPI - Information](https://www.mdpi.com/journal/information)\n- [MDPI - Robotics](https://www.mdpi.com/journal/robotics)\n- [Springer - Applied Intelligence](https://link.springer.com/journal/10489)\n- [Springer - Artificial Intelligence Review](https://link.springer.com/journal/10462)\n- [Springer - Autonomous Agents and Multi-Agent Systems](http://www.springer.com/journal/10458)\n- [Springer - Computational and Mathematical Organization Theory](http://www.springer.com/journal/10588)\n- [Springer - Discover Artificial Intelligence](https://link.springer.com/journal/44163)\n- [Springer - Evolutionary Intelligence](https://link.springer.com/journal/12065)\n- [Springer - Journal of Automated Reasoning](http://www.springer.com/journal/10817)\n- [Springer - Journal of Intelligent Information Systems](https://link.springer.com/journal/10844)\n- [Springer - Minds and Machines](http://www.springer.com/journal/11023)\n- [Springer - New Generation Computing](https://link.springer.com/journal/354)\n- [Springer - Progress in Artificial Intelligence](https://link.springer.com/journal/13748)\n- [ScienceDirect - AI Open](https://www.sciencedirect.com/journal/ai-open)\n- [ScienceDirect - Array](https://www.sciencedirect.com/journal/array)\n- [ScienceDirect - Cognitive Robotics](https://www.sciencedirect.com/journal/cognitive-robotics)\n- [ScienceDirect - Future Computing and Informatics Journal](https://www.sciencedirect.com/journal/future-computing-and-informatics-journal)\n- [ScienceDirect - High-Confidence Computing Journal](https://www.sciencedirect.com/journal/high-confidence-computing)\n- [ScienceDirect - Intelligent Systems with Applications](https://www.sciencedirect.com/journal/intelligent-systems-with-applications)\n- [ScienceDirect - International Journal of Cognitive Computing in Engineering](https://www.sciencedirect.com/journal/international-journal-of-cognitive-computing-in-engineering)\n- [ScienceDirect - International Journal of Intelligent Networks](https://www.sciencedirect.com/journal/international-journal-of-intelligent-networks)\n- [ScienceDirect - Internet of Things and Cyber-Physical Systems](https://www.sciencedirect.com/journal/internet-of-things-and-cyber-physical-systems)\n- [ScienceDirect - Journal of Automation and Intelligence](https://www.sciencedirect.com/journal/journal-of-automation-and-intelligence)\n- [ScienceDirect - Journal of Information and Intelligence](https://www.sciencedirect.com/journal/journal-of-information-and-intelligence)\n- [ScienceDirect - Machine Learning with Applications](https://www.sciencedirect.com/journal/machine-learning-with-applications)\n- [ScienceDirect - SoftwareX](https://www.sciencedirect.com/journal/softwarex)\n- [ScienceDirect - Systems and Soft Computing](https://www.sciencedirect.com/journal/systems-and-soft-computing)\n- [ScienceDirect - Virtual Reality \u0026 Intelligent Hardware](https://www.sciencedirect.com/journal/virtual-reality-and-intelligent-hardware)\n\n## 🧠 LLMs\n- [Build a Large Language Model (From Scratch) / Sebastian Raschka](https://www.manning.com/books/build-a-large-language-model-from-scratch)\n- [Generative AI on AWS / Chris Fregly, Antje Barth, Shelbee Eigenbrode](https://www.oreilly.com/library/view/generative-ai-on/9781098159214/)\n- [Large Language Models at Work - Enhancing Software Systems with Language Models / Vlad Rișcuția](https://vladris.com/llm-book)\n- [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/)\n- [Practical Natural Language Processing](https://www.oreilly.com/library/view/practical-natural-language/9781492054047/)\n- [Speech and Language Processing (3rd ed. draft) / Dan Jurafsky and James H. Martin](https://web.stanford.edu/~jurafsky/slp3/)\n- [Understanding Large Language Models / Thimira Amaratunga](https://link.springer.com/book/10.1007/979-8-8688-0017-7?locale=en-fr\u0026gad_source=1)\n\n### Awesome Research Papers\n- [A Comprehensive Overview of Large Language Models](http://arxiv.org/abs/2307.06435)\n- [A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks](https://arxiv.org/abs/2407.12994)\n- [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155)\n- [Automated Design of Agentic Systems](https://arxiv.org/abs/2408.08435)\n- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://research.google/pubs/bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding/)\n- [Beyond Text: A Deep Dive into Large Language Models’ Ability on Understanding Graph Data](http://arxiv.org/abs/2310.04944)\n- [Cramming: Training a Language Model on a Single GPU in One Day](http://arxiv.org/abs/2212.14034)\n- [Discovering Latent Knowledge in Language Models Without Supervision](https://arxiv.org/abs/2212.03827)\n- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://arxiv.org/abs/2309.06180)\n- [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442)\n- [Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions](https://arxiv.org/abs/2305.10435)\n- [GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables](https://dl.acm.org/doi/10.1145/3656409)\n- [MathChat: Converse to Tackle Challenging Math Problems with LLM Agents](https://arxiv.org/abs/2306.01337)\n- [LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference](https://arxiv.org/abs/2407.14057)\n- [LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code Generation](https://arxiv.org/abs/2308.02828)\n- [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629)\n- [Representation Deficiency in Masked Language Modeling](https://arxiv.org/abs/2302.02060)\n- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](http://arxiv.org/abs/2303.12712)\n- [SpreadsheetLLM: Encoding Spreadsheets for Large Language Models](https://arxiv.org/abs/2407.09025)\n- [StarCoder: may the source be with you!](10.48550/arXiv.2305.06161)\n- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://arxiv.org/abs/2408.06292)\n- [Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556)\n\n## 🕸️ Articles on Neural Networks and Related Topics\n- [An Overview of Large Language Models (LLMs)](https://wandb.ai/mostafaibrahim17/ml-articles/reports/An-Overview-of-Large-Language-Models-LLMs---VmlldzozODA3MzQz)\n- [Introducing deep learning and long-short term memory networks / IBM](https://developer.ibm.com/tutorials/iot-deep-learning-anomaly-detection-1/)\n- [Making Peace with LLM Non-determinism](https://barryzhang.substack.com/p/making-peace-with-llm-non-determinism)\n- [Non-determinism in GPT-4 is caused by Sparse MoE](https://152334h.github.io/blog/non-determinism-in-gpt-4/)\n- [Sampling for Text Generation](https://huyenchip.com/2024/01/16/sampling.html)\n- [The Annotated Transformer](http://nlp.seas.harvard.edu/annotated-transformer/#model-architecture)\n- [What are recurrent neural networks? / IBM](https://www.ibm.com/topics/recurrent-neural-networks)\n\n### Articles by Andrej Karpathy\n- [A Recipe for Training Neural Networks / Andrej Karpathy](https://karpathy.github.io/2019/04/25/recipe/)\n- [Neural Networks: Zero to Hero / Andrej Karpathy](https://karpathy.ai/zero-to-hero.html)\n\n### Articles by George Ho\n- [Autoregressive Models in Deep Learning — A Brief Survey](https://www.georgeho.org/deep-autoregressive-models/)\n- [Cookbook — Bayesian Modelling with PyMC3](https://www.georgeho.org/bayesian-modelling-cookbook/)\n- [Decaying Evidence and Contextual Bandits — Bayesian Reinforcement Learning (Part 2)](https://www.georgeho.org/bayesian-bandits-2/)\n- [Floating-Point Formats and Deep Learning](https://www.georgeho.org/floating-point-deep-learning/)\n- [Fruit Loops and Learning - The LUPI Paradigm and SVM+](https://www.georgeho.org/lupi/)\n- [Linear Discriminant Analysis for Starters](https://www.georgeho.org/lda/)\n- [Modern Computational Methods for Bayesian Inference — A Reading List](https://www.georgeho.org/bayesian-inference-reading/)\n- [Multi-Armed Bandits and Conjugate Models — Bayesian Reinforcement Learning (Part 1)](https://www.georgeho.org/bayesian-bandits/)\n- [Portfolio Risk Analytics and Performance Attribution with Pyfolio](https://www.georgeho.org/pyfolio/)\n- [Probabilistic and Bayesian Matrix Factorizations for Text Clustering](https://www.georgeho.org/matrix-factorizations/)\n- [Transformers in Natural Language Processing — A Brief Survey](https://www.georgeho.org/transformers-in-nlp/)\n- [Understanding Hate Speech on Reddit through Text Clustering](https://www.georgeho.org/reddit-clusters/)\n- [What I Wish Someone Had Told Me About Tensor Computation Libraries](https://www.georgeho.org/tensor-computation-libraries/)\n- [Why Latent Dirichlet Allocation Sucks](https://www.georgeho.org/lda-sucks/)\n  \n### Articles by Jay Alammar\n- [A Visual And Interactive Look at Basic Neural Network Math](https://jalammar.github.io/feedforward-neural-networks-visual-interactive/)\n- [A Visual Intro to NumPy and Data Representation](https://jalammar.github.io/visual-numpy/)\n- [Finding the Words to Say: Hidden State Visualizations for Language Models](https://jalammar.github.io/hidden-states/)\n- [How GPT3 Works - Visualizations and Animations](https://jalammar.github.io/how-gpt3-works-visualizations-animations/)\n- [Interfaces for Explaining Transformer Language Models](https://jalammar.github.io/explaining-transformers/)\n- [The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)](https://jalammar.github.io/illustrated-bert/)\n- [The Illustrated Retrieval Transformer](https://jalammar.github.io/illustrated-retrieval-transformer/)\n- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)\n- [The Illustrated Word2vec](https://jalammar.github.io/illustrated-word2vec/)\n- [Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/)\n- [Visualizing Pandas' Pivoting and Reshaping Functions](https://jalammar.github.io/visualizing-pandas-pivoting-and-reshaping/)\n\n### Distill Scientific Journal\n\n- [Understanding Convolutions on Graphs](https://distill.pub/2021/understanding-gnns/)\n- [A Gentle Introduction to Graph Neural Networks](https://distill.pub/2021/gnn-intro/)\n- [Multimodal Neurons in Artificial Neural Networks](https://distill.pub/2021/multimodal-neurons/)\n- [Understanding RL Vision](https://distill.pub/2020/understanding-rl-vision/)\n- [Communicating with Interactive Articles](https://distill.pub/2020/communicating-with-interactive-articles/)\n- [Thread: Differentiable Self-organizing Systems](https://distill.pub/2020/selforg/)\n- [Exploring Bayesian Optimization](https://distill.pub/2020/bayesian-optimization/)\n- [Visualizing Neural Networks with the Grand Tour](https://distill.pub/2020/grand-tour/)\n- [Thread: Circuits](https://distill.pub/2020/circuits/)\n- [Visualizing the Impact of Feature Attribution Baselines](https://distill.pub/2020/attribution-baselines/)\n- [Computing Receptive Fields of Convolutional Neural Networks](https://distill.pub/2019/computing-receptive-fields/)\n- [The Paths Perspective on Value Learning](https://distill.pub/2019/paths-perspective-on-value-learning/)\n- [A Discussion of Adversarial Examples Are Not Bugs, They Are Features](https://distill.pub/2019/advex-bugs-discussion/)\n- [Open Questions about Generative Adversarial Networks](https://distill.pub/2019/gan-open-problems/)\n- [A Visual Exploration of Gaussian Processes](https://distill.pub/2019/visual-exploration-gaussian-processes/)\n- [Visualizing memorization in RNNs](https://distill.pub/2019/memorization-in-rnns/)\n- [Exploring Neural Networks with Activation Atlases](https://distill.pub/2019/activation-atlas/)\n- [AI Safety Needs Social Scientists](https://distill.pub/2019/safety-needs-social-scientists/)\n- [Differentiable Image Parameterizations](https://distill.pub/2018/differentiable-parameterizations/)\n- [Feature-wise transformations](https://distill.pub)\n- [The Building Blocks of Interpretability](https://distill.pub/2018/building-blocks/)\n- [Using Artificial Intelligence to Augment Human Intelligence](https://distill.pub/2017/aia/)\n- [Sequence Modeling With CTC](https://distill.pub/2017/ctc/)\n- [Feature Visualization](https://distill.pub/2017/feature-visualization/)\n- [Why Momentum Really Works](https://distill.pub/2017/momentum/)\n- [Four Experiments in Handwriting with a Neural Network](https://distill.pub/2016/handwriting/)\n- [Deconvolution and Checkerboard Artifacts](https://distill.pub/2016/deconv-checkerboard/)\n- [How to Use t-SNE Effectively](https://distill.pub/2016/misread-tsne/)\n- [Attention and Augmented Recurrent Neural Networks](https://distill.pub/2016/augmented-rnns/)\n  \n## 👻 Neural Networks Visualizations\n- [Attention Viz](https://attentionviz.com)\n- [BertViz](https://github.com/jessevig/bertviz)\n- [LLM Visualization](https://bbycroft.net/llm)\n- [Netron](https://github.com/lutzroeder/netron?tab=readme-ov-file)\n- [Neural Network Visualization](https://github.com/julrog/nn_vis)\n- [TensorBoard: TensorFlow's visualization toolkit](https://www.tensorflow.org/tensorboard)\n- [TensorFlow Playground](https://playground.tensorflow.org)\n- [TensorFlow Embedding Projector](http://projector.tensorflow.org)\n- [Open sourcing the Embedding Projector: a tool for visualizing high dimensional data](https://research.google/blog/open-sourcing-the-embedding-projector-a-tool-for-visualizing-high-dimensional-data/)\n\n## 🐍 Python\n- [Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing](https://www.oreilly.com/library/view/applied-natural-language/9781484237335/)\n- [Artificial Intelligence Programming with Python](https://www.oreilly.com/library/view/artificial-intelligence-programming/9781119820864/)\n- [Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python](https://www.oreilly.com/library/view/building-computer-vision/9781484258873/)\n- [Building LLM Powered Applications](https://www.oreilly.com/library/view/building-llm-powered/9781835462317/)\n- [Building LLMs for Production](https://www.oreilly.com/library/view/building-llms-for/9798324731472/)\n- [Building Recommendation Systems in Python and JAX](https://www.oreilly.com/library/view/building-recommendation-systems/9781492097983/)\n- [Conversational AI](https://www.oreilly.com/library/view/conversational-ai/9781617298837/)\n- [Deep Learning for Natural Language Processing](https://www.oreilly.com/library/view/deep-learning-for/9781617295447/)\n- [Deep Learning for Time Series Cookbook](https://www.oreilly.com/library/view/deep-learning-for/9781805129233/)\n- [Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras](https://www.oreilly.com/library/view/deep-learning-with/9781484235164/)\n- [Deep Learning with PyTorch / Eli Stevens, Luca Antiga, and Thomas Viehmann](https://www.manning.com/books/deep-learning-with-pytorch)\n- [Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models](https://www.oreilly.com/library/view/deep-reinforcement-learning/9798868802737/)\n- [Explainable AI for Practitioners](https://www.oreilly.com/library/view/explainable-ai-for/9781098119126/)\n- [Foundations of Deep Reinforcement Learning: Theory and Practice in Python](https://www.oreilly.com/library/view/foundations-of-deep/9780135172490/)\n- [Generative Deep Learning, 2nd Edition](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/)\n- [Hands-On Genetic Algorithms with Python - Second Edition](https://www.oreilly.com/library/view/hands-on-genetic-algorithms/9781805123798/)\n- [Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch](https://www.oreilly.com/library/view/hands-on-machine-learning/9781484279212/)\n- [Inside Deep Learning](https://www.oreilly.com/library/view/inside-deep-learning/9781617298639/)\n- [Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python](https://www.oreilly.com/library/view/learn-keras-for/9781484242407/)\n- [Low-Code AI](https://www.oreilly.com/library/view/low-code-ai/9781098146818/)\n- [Machine Learning Approach for Cloud Data Analytics in IoT](https://www.oreilly.com/library/view/machine-learning-approach/9781119785804/)\n- [Machine Learning Engineering in Action](https://www.oreilly.com/library/view/machine-learning-engineering/9781617298714/)\n- [Machine Learning with Python Cookbook, 2nd Edition](https://www.oreilly.com/library/view/machine-learning-with/9781098135713/)\n- [Natural Language Processing with Transformers, Revised Edition](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/)\n- [Practical Deep Learning at Scale with MLflow](https://www.oreilly.com/library/view/practical-deep-learning/9781803241333/)\n- [Practical Natural Language Processing](https://www.oreilly.com/library/view/practical-natural-language/9781492054047/)\n- [Programming Large Language Models with Azure Open AI: Conversational programming and prompt engineering with LLMs](https://www.oreilly.com/library/view/programming-large-language/9780138280383/)\n  \n## ⛓️ ML \u0026 AI Frameworks\n- [FLAX](https://flax.readthedocs.io/en/latest/)\n- [JAX](https://jax.readthedocs.io/en/latest/)\n- [Keras](https://keras.io)\n- [MLX](https://ml-explore.github.io/mlx/build/html/install.html)\n- [PyTorch](https://pytorch.org)\n- [TensorFlow](https://www.tensorflow.org)\n- [Micrograd](https://github.com/karpathy/micrograd)\n- [Tinygrad](https://github.com/tinygrad/tinygrad)\n\n## ⚙️ SDKs and Development Tools\n- [AutoGEN](https://microsoft.github.io/autogen/)\n- [LangChain](https://www.langchain.com)\n- [LlamaIndex](https://docs.llamaindex.ai/en/stable/)\n- [Ollama](https://ollama.com)\n- [vLLM](https://docs.vllm.ai/en/latest/)\n\n## 🎞 WWDC24 | Apple\n- [WWDC24: Bring your machine learning and AI models to Apple silicon | Apple](https://www.youtube.com/watch?v=ZgG2JVnJ7nw)\n- [WWDC24: Deploy machine learning and AI models on-device with Core ML | Apple](https://www.youtube.com/watch?v=aawk4l9W9YU)\n- [WWDC24: Explore machine learning on Apple platforms | Apple](https://www.youtube.com/watch?v=p_hyo2FRil4)\n- [WWDC24: Support real-time ML inference on the CPU | Apple](https://www.youtube.com/watch?v=VdLgH2nJMBg)\n- [WWDC24: Train your machine learning and AI models on Apple GPUs | Apple](https://www.youtube.com/watch?v=CbmTFTsbyPI)\n- [WWDC24: What’s new in Create ML | Apple](https://www.youtube.com/watch?v=yjblfqwR37s)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Folehxch%2Fai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Folehxch%2Fai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Folehxch%2Fai/lists"}