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🧠 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.
https://github.com/olehxch/ai

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🧠 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.

Awesome Lists containing this project

README 

          
# 🧬 Artificial Intelligence - Science, Research & Engineering



✨ 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.



⚑️ 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:



[My Articles on Medium](https://medium.com/@olehch)



πŸ™Œ This collection was created by Oleh Chaplia and is constantly updated.



## πŸ‘¨β€πŸ”¬ Commercial AI Research Labs

- [Archetype AI](https://www.archetypeai.io)

- [Artificial Intelligence Research Lab](https://www.bell-labs.com/research-innovation/projects-and-initiatives/air-lab/)

- [Cohere For AI](https://cohere.com/research)

- [Cortical Labs - Dishbrain Intelligence](https://corticallabs.com)

- [Figure.AI](https://www.figure.ai)

- [FinalSpark](https://finalspark.com)

- [Google AI Lab](https://labs.google)

- [Google DeepMind Research](https://deepmind.com/research/)

- [IBM Research](https://research.ibm.com)

- [Intel AI Lab](https://www.intel.com/content/www/us/en/research/ai.html)

- [Machine Intelligence Research Institute](https://intelligence.org/research-guide/)

- [Machine Learning | Research at Apple](https://machinelearning.apple.com)

- [Meta Research](https://research.facebook.com)

- [Microsoft Research](https://www.microsoft.com/en-us/research/)

- [NASA - Autonomous Systems & Robotics](https://www.nasa.gov/intelligent-systems-division/autonomous-systems-and-robotics/)

- [NASA - Intelligent Systems Division](https://www.nasa.gov/intelligent-systems-division/)

- [NASA - Robust Software Engineering](https://www.nasa.gov/intelligent-systems-division/robust-software-engineering/)

- [Nvidia Resources](https://developer.nvidia.com)

- [OpenAI](https://openai.com/about/)

- [Silo AI: Europe's largest private AI lab](https://www.silo.ai)



## πŸ‘¨β€πŸ”¬ Academic AI Research Labs

- [Berkeley Artificial Intelligence Research](https://bair.berkeley.edu)

- [Machine Learning Research Group, University of Oxford](https://www.robots.ox.ac.uk/~parg/)

- [MIT-IBM Watson Research Lab](https://mitibmwatsonailab.mit.edu)

- [MIT Computer Science & Artificial Intelligence Laboratory](https://www.csail.mit.edu)

- [The Alan Turing Institute](https://www.turing.ac.uk)

- [Stanford AI Lab](https://ai.stanford.edu)



## πŸ€– Artificial Intelligence



### Books on AI

- [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/)

- [AI Engineering](https://www.oreilly.com/library/view/ai-engineering/9781098166298/)

- [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)

- [Apps with GPT-4 and ChatGPT](https://appswithgpt.com)

- [Applied Machine Learning and AI for Engineers](https://www.oreilly.com/library/view/applied-machine-learning/9781492098041/)

- [Applied Machine Learning Explainability Techniques](https://www.oreilly.com/library/view/applied-machine-learning/9781803246154/)

- [Artificial Intelligence - Ethical, social, and security impacts for the present and the future](https://www.oreilly.com/library/view/artificial-intelligence/9781787783720/)

- [Artificial Intelligence Illuminated / Ben Coppin](http://futuresoft.yolasite.com/resources/Artificial%20Intelligence%20Illuminated.pdf)

- [Artificial Intelligence: A Modern Approach. Third Edition / Stuart Russell & Peter Norvig](https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf)

- [Artificial Intelligence: Foundations of Computational Agents, 3rd Edition](https://artint.info/3e/html/ArtInt3e.html)

- [Bio-Inspired Artificial Intelligence. Theories, Methods, and Technologies / Dario Floreano & Claudio Mattiussi](https://mitpress.mit.edu/9780262547734/bio-inspired-artificial-intelligence/)

- [Competing in the Age of AI](https://www.oreilly.com/library/view/competing-in-the/9781633697638/)

- [Deep Blueberry Book](https://mithi.github.io/deep-blueberry/)

- [Deep Learning](https://www.deeplearningbook.org)

- [Designing Autonomous AI](https://www.oreilly.com/library/view/designing-autonomous-ai/9781098110741/)

- [Distributed Machine Learning Patterns / Yuan Tang](https://www.manning.com/books/distributed-machine-learning-patterns)

- [Dive into Deep Learning](https://d2l.ai)

- [Effective Machine Learning Teams](https://www.oreilly.com/library/view/effective-machine-learning/9781098144623/)

- [Essential Math for AI](https://www.oreilly.com/library/view/essential-math-for/9781098107628/)

- [Generative AI for Software Development](https://www.oreilly.com/library/view/generative-ai-for/9781098162269/)

- [Grokking Artificial Intelligence Algorithms](https://www.oreilly.com/library/view/grokking-artificial-intelligence/9781617296185/?_gl=1*1q3ra89*_ga*MjAzNjIyODkxLjE2OTUwOTc5NDQ.*_ga_092EL089CH*MTY5NTU5MjIyMy4zLjEuMTY5NTU5NDExMi42MC4wLjA)

- [How Machine Learning Works](https://livebook.manning.com/book/how-machine-learning-works/welcome/v-5)

- [Inside Deep Learning](https://www.oreilly.com/library/view/inside-deep-learning/9781617298639/)

- [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)

- [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/)

- [Machine Learning Model Serving Patterns and Best Practices](https://www.oreilly.com/library/view/machine-learning-model/9781803249902/)

- [Math and Architectures of Deep Learning / Krishnendu Chaudhury](https://www.manning.com/books/math-and-architectures-of-deep-learning)

- [Networking Vehicles to Everything](https://www.oreilly.com/library/view/networking-vehicles-to/9781501507205/)

- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)

- [Practical Machine Learning for Computer Vision](https://www.oreilly.com/library/view/practical-machine-learning/9781098102357/)

- [Privacy-Preserving Machine Learning](https://www.oreilly.com/library/view/privacy-preserving-machine-learning/9781800564671/)

- [Prompt Engineering for LLMs](https://www.oreilly.com/library/view/prompt-engineering-for/9781098156145/)

- [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/)

- [The AI Revolution in Medicine: GPT-4 and Beyond](https://www.oreilly.com/library/view/the-ai-revolution/9780138200145/)

- [The Machine Learning Solutions Architect Handbook - Second Edition](https://www.oreilly.com/library/view/the-machine-learning/9781805122500/)

- [What Is LLMOps?](https://www.oreilly.com/library/view/what-is-llmops/9781098154301/)

- [The Software-Defined Vehicle](https://www.oreilly.com/library/view/the-software-defined-vehicle/9781098157814/)



### Swarm Intelligence



- [Autonomous Mobile Robots and Multi-Robot Systems](https://www.oreilly.com/library/view/autonomous-mobile-robots/9781119212867/)

- [Bio-Inspired Computing and Networking](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)

- [Deep Learning for Unmanned Systems](https://link.springer.com/book/10.1007/978-3-030-77939-9)

- [Genetic Algorithms and Machine Learning for Programmers](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)

- [GPU-based Parallel Implementation of Swarm Intelligence Algorithms](https://www.oreilly.com/library/view/gpu-based-parallel-implementation/9780128093641/)

- [Nature-Inspired Optimization Algorithms](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)

- [Swarm Intelligence](https://www.oreilly.com/library/view/swarm-intelligence/9781119865063/)

- [Swarm Intelligence](https://www.oreilly.com/library/view/swarm-intelligence/9781000529753/)

- [Swarm Intelligence Algorithms](https://www.oreilly.com/library/view/swarm-intelligence-algorithms/9780429749506/)

- [Swarm Intelligence Algorithms (Two Volume Set)](https://www.oreilly.com/library/view/swarm-intelligence-algorithms/9781000168747/)

- [Swarm Intelligence and Bio-Inspired Computation](https://www.oreilly.com/library/view/swarm-intelligence-and/9780124051638/)



### Open Journals on AI



- [MDPI - AI](https://www.mdpi.com/journal/ai)

- [MDPI - Big Data and Cognitive Computing](https://www.mdpi.com/journal/BDCC)

- [MDPI - Computers](https://www.mdpi.com/journal/computers)

- [MDPI - Data](https://www.mdpi.com/journal/data)

- [MDPI - Drones](https://www.mdpi.com/journal/drones)

- [MDPI - Future Internet](https://www.mdpi.com/journal/futureinternet)

- [MDPI - Informatics](https://www.mdpi.com/journal/informatics)

- [MDPI - Information](https://www.mdpi.com/journal/information)

- [MDPI - Robotics](https://www.mdpi.com/journal/robotics)

- [Springer - Applied Intelligence](https://link.springer.com/journal/10489)

- [Springer - Artificial Intelligence Review](https://link.springer.com/journal/10462)

- [Springer - Autonomous Agents and Multi-Agent Systems](http://www.springer.com/journal/10458)

- [Springer - Computational and Mathematical Organization Theory](http://www.springer.com/journal/10588)

- [Springer - Discover Artificial Intelligence](https://link.springer.com/journal/44163)

- [Springer - Evolutionary Intelligence](https://link.springer.com/journal/12065)

- [Springer - Journal of Automated Reasoning](http://www.springer.com/journal/10817)

- [Springer - Journal of Intelligent Information Systems](https://link.springer.com/journal/10844)

- [Springer - Minds and Machines](http://www.springer.com/journal/11023)

- [Springer - New Generation Computing](https://link.springer.com/journal/354)

- [Springer - Progress in Artificial Intelligence](https://link.springer.com/journal/13748)

- [ScienceDirect - AI Open](https://www.sciencedirect.com/journal/ai-open)

- [ScienceDirect - Array](https://www.sciencedirect.com/journal/array)

- [ScienceDirect - Cognitive Robotics](https://www.sciencedirect.com/journal/cognitive-robotics)

- [ScienceDirect - Future Computing and Informatics Journal](https://www.sciencedirect.com/journal/future-computing-and-informatics-journal)

- [ScienceDirect - High-Confidence Computing Journal](https://www.sciencedirect.com/journal/high-confidence-computing)

- [ScienceDirect - Intelligent Systems with Applications](https://www.sciencedirect.com/journal/intelligent-systems-with-applications)

- [ScienceDirect - International Journal of Cognitive Computing in Engineering](https://www.sciencedirect.com/journal/international-journal-of-cognitive-computing-in-engineering)

- [ScienceDirect - International Journal of Intelligent Networks](https://www.sciencedirect.com/journal/international-journal-of-intelligent-networks)

- [ScienceDirect - Internet of Things and Cyber-Physical Systems](https://www.sciencedirect.com/journal/internet-of-things-and-cyber-physical-systems)

- [ScienceDirect - Journal of Automation and Intelligence](https://www.sciencedirect.com/journal/journal-of-automation-and-intelligence)

- [ScienceDirect - Journal of Information and Intelligence](https://www.sciencedirect.com/journal/journal-of-information-and-intelligence)

- [ScienceDirect - Machine Learning with Applications](https://www.sciencedirect.com/journal/machine-learning-with-applications)

- [ScienceDirect - SoftwareX](https://www.sciencedirect.com/journal/softwarex)

- [ScienceDirect - Systems and Soft Computing](https://www.sciencedirect.com/journal/systems-and-soft-computing)

- [ScienceDirect - Virtual Reality & Intelligent Hardware](https://www.sciencedirect.com/journal/virtual-reality-and-intelligent-hardware)



## 🧠 LLMs

- [Build a Large Language Model (From Scratch) / Sebastian Raschka](https://www.manning.com/books/build-a-large-language-model-from-scratch)

- [Generative AI on AWS / Chris Fregly, Antje Barth, Shelbee Eigenbrode](https://www.oreilly.com/library/view/generative-ai-on/9781098159214/)

- [Large Language Models at Work - Enhancing Software Systems with Language Models / Vlad RiΘ™cuΘ›ia](https://vladris.com/llm-book)

- [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/)

- [Practical Natural Language Processing](https://www.oreilly.com/library/view/practical-natural-language/9781492054047/)

- [Speech and Language Processing (3rd ed. draft) / Dan Jurafsky and James H. Martin](https://web.stanford.edu/~jurafsky/slp3/)

- [Understanding Large Language Models / Thimira Amaratunga](https://link.springer.com/book/10.1007/979-8-8688-0017-7?locale=en-fr&gad_source=1)



### Awesome Research Papers

- [A Comprehensive Overview of Large Language Models](http://arxiv.org/abs/2307.06435)

- [A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks](https://arxiv.org/abs/2407.12994)

- [Attention Is All You Need](https://arxiv.org/abs/1706.03762)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155)

- [Automated Design of Agentic Systems](https://arxiv.org/abs/2408.08435)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://research.google/pubs/bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding/)

- [Beyond Text: A Deep Dive into Large Language Models’ Ability on Understanding Graph Data](http://arxiv.org/abs/2310.04944)

- [Cramming: Training a Language Model on a Single GPU in One Day](http://arxiv.org/abs/2212.14034)

- [Discovering Latent Knowledge in Language Models Without Supervision](https://arxiv.org/abs/2212.03827)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://arxiv.org/abs/2309.06180)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442)

- [Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions](https://arxiv.org/abs/2305.10435)

- [GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables](https://dl.acm.org/doi/10.1145/3656409)

- [MathChat: Converse to Tackle Challenging Math Problems with LLM Agents](https://arxiv.org/abs/2306.01337)

- [LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference](https://arxiv.org/abs/2407.14057)

- [LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code Generation](https://arxiv.org/abs/2308.02828)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629)

- [Representation Deficiency in Masked Language Modeling](https://arxiv.org/abs/2302.02060)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](http://arxiv.org/abs/2303.12712)

- [SpreadsheetLLM: Encoding Spreadsheets for Large Language Models](https://arxiv.org/abs/2407.09025)

- [StarCoder: may the source be with you!](10.48550/arXiv.2305.06161)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://arxiv.org/abs/2408.06292)

- [Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556)



## πŸ•ΈοΈ Articles on Neural Networks and Related Topics

- [An Overview of Large Language Models (LLMs)](https://wandb.ai/mostafaibrahim17/ml-articles/reports/An-Overview-of-Large-Language-Models-LLMs---VmlldzozODA3MzQz)

- [Introducing deep learning and long-short term memory networks / IBM](https://developer.ibm.com/tutorials/iot-deep-learning-anomaly-detection-1/)

- [Making Peace with LLM Non-determinism](https://barryzhang.substack.com/p/making-peace-with-llm-non-determinism)

- [Non-determinism in GPT-4 is caused by Sparse MoE](https://152334h.github.io/blog/non-determinism-in-gpt-4/)

- [Sampling for Text Generation](https://huyenchip.com/2024/01/16/sampling.html)

- [The Annotated Transformer](http://nlp.seas.harvard.edu/annotated-transformer/#model-architecture)

- [What are recurrent neural networks? / IBM](https://www.ibm.com/topics/recurrent-neural-networks)



### Articles by Andrej Karpathy

- [A Recipe for Training Neural Networks / Andrej Karpathy](https://karpathy.github.io/2019/04/25/recipe/)

- [Neural Networks: Zero to Hero / Andrej Karpathy](https://karpathy.ai/zero-to-hero.html)



### Articles by George Ho

- [Autoregressive Models in Deep Learning β€” A Brief Survey](https://www.georgeho.org/deep-autoregressive-models/)

- [Cookbook β€” Bayesian Modelling with PyMC3](https://www.georgeho.org/bayesian-modelling-cookbook/)

- [Decaying Evidence and Contextual Bandits β€” Bayesian Reinforcement Learning (Part 2)](https://www.georgeho.org/bayesian-bandits-2/)

- [Floating-Point Formats and Deep Learning](https://www.georgeho.org/floating-point-deep-learning/)

- [Fruit Loops and Learning - The LUPI Paradigm and SVM+](https://www.georgeho.org/lupi/)

- [Linear Discriminant Analysis for Starters](https://www.georgeho.org/lda/)

- [Modern Computational Methods for Bayesian Inference β€” A Reading List](https://www.georgeho.org/bayesian-inference-reading/)

- [Multi-Armed Bandits and Conjugate Models β€” Bayesian Reinforcement Learning (Part 1)](https://www.georgeho.org/bayesian-bandits/)

- [Portfolio Risk Analytics and Performance Attribution with Pyfolio](https://www.georgeho.org/pyfolio/)

- [Probabilistic and Bayesian Matrix Factorizations for Text Clustering](https://www.georgeho.org/matrix-factorizations/)

- [Transformers in Natural Language Processing β€” A Brief Survey](https://www.georgeho.org/transformers-in-nlp/)

- [Understanding Hate Speech on Reddit through Text Clustering](https://www.georgeho.org/reddit-clusters/)

- [What I Wish Someone Had Told Me About Tensor Computation Libraries](https://www.georgeho.org/tensor-computation-libraries/)

- [Why Latent Dirichlet Allocation Sucks](https://www.georgeho.org/lda-sucks/)

  

### Articles by Jay Alammar

- [A Visual And Interactive Look at Basic Neural Network Math](https://jalammar.github.io/feedforward-neural-networks-visual-interactive/)

- [A Visual Intro to NumPy and Data Representation](https://jalammar.github.io/visual-numpy/)

- [Finding the Words to Say: Hidden State Visualizations for Language Models](https://jalammar.github.io/hidden-states/)

- [How GPT3 Works - Visualizations and Animations](https://jalammar.github.io/how-gpt3-works-visualizations-animations/)

- [Interfaces for Explaining Transformer Language Models](https://jalammar.github.io/explaining-transformers/)

- [The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)](https://jalammar.github.io/illustrated-bert/)

- [The Illustrated Retrieval Transformer](https://jalammar.github.io/illustrated-retrieval-transformer/)

- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)

- [The Illustrated Word2vec](https://jalammar.github.io/illustrated-word2vec/)

- [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/)

- [Visualizing Pandas' Pivoting and Reshaping Functions](https://jalammar.github.io/visualizing-pandas-pivoting-and-reshaping/)



### Distill Scientific Journal



- [Understanding Convolutions on Graphs](https://distill.pub/2021/understanding-gnns/)

- [A Gentle Introduction to Graph Neural Networks](https://distill.pub/2021/gnn-intro/)

- [Multimodal Neurons in Artificial Neural Networks](https://distill.pub/2021/multimodal-neurons/)

- [Understanding RL Vision](https://distill.pub/2020/understanding-rl-vision/)

- [Communicating with Interactive Articles](https://distill.pub/2020/communicating-with-interactive-articles/)

- [Thread: Differentiable Self-organizing Systems](https://distill.pub/2020/selforg/)

- [Exploring Bayesian Optimization](https://distill.pub/2020/bayesian-optimization/)

- [Visualizing Neural Networks with the Grand Tour](https://distill.pub/2020/grand-tour/)

- [Thread: Circuits](https://distill.pub/2020/circuits/)

- [Visualizing the Impact of Feature Attribution Baselines](https://distill.pub/2020/attribution-baselines/)

- [Computing Receptive Fields of Convolutional Neural Networks](https://distill.pub/2019/computing-receptive-fields/)

- [The Paths Perspective on Value Learning](https://distill.pub/2019/paths-perspective-on-value-learning/)

- [A Discussion of Adversarial Examples Are Not Bugs, They Are Features](https://distill.pub/2019/advex-bugs-discussion/)

- [Open Questions about Generative Adversarial Networks](https://distill.pub/2019/gan-open-problems/)

- [A Visual Exploration of Gaussian Processes](https://distill.pub/2019/visual-exploration-gaussian-processes/)

- [Visualizing memorization in RNNs](https://distill.pub/2019/memorization-in-rnns/)

- [Exploring Neural Networks with Activation Atlases](https://distill.pub/2019/activation-atlas/)

- [AI Safety Needs Social Scientists](https://distill.pub/2019/safety-needs-social-scientists/)

- [Differentiable Image Parameterizations](https://distill.pub/2018/differentiable-parameterizations/)

- [Feature-wise transformations](https://distill.pub)

- [The Building Blocks of Interpretability](https://distill.pub/2018/building-blocks/)

- [Using Artificial Intelligence to Augment Human Intelligence](https://distill.pub/2017/aia/)

- [Sequence Modeling With CTC](https://distill.pub/2017/ctc/)

- [Feature Visualization](https://distill.pub/2017/feature-visualization/)

- [Why Momentum Really Works](https://distill.pub/2017/momentum/)

- [Four Experiments in Handwriting with a Neural Network](https://distill.pub/2016/handwriting/)

- [Deconvolution and Checkerboard Artifacts](https://distill.pub/2016/deconv-checkerboard/)

- [How to Use t-SNE Effectively](https://distill.pub/2016/misread-tsne/)

- [Attention and Augmented Recurrent Neural Networks](https://distill.pub/2016/augmented-rnns/)

  

## πŸ‘» Neural Networks Visualizations

- [Attention Viz](https://attentionviz.com)

- [BertViz](https://github.com/jessevig/bertviz)

- [LLM Visualization](https://bbycroft.net/llm)

- [Netron](https://github.com/lutzroeder/netron?tab=readme-ov-file)

- [Neural Network Visualization](https://github.com/julrog/nn_vis)

- [TensorBoard: TensorFlow's visualization toolkit](https://www.tensorflow.org/tensorboard)

- [TensorFlow Playground](https://playground.tensorflow.org)

- [TensorFlow Embedding Projector](http://projector.tensorflow.org)

- [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/)



## 🐍 Python

- [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/)

- [Artificial Intelligence Programming with Python](https://www.oreilly.com/library/view/artificial-intelligence-programming/9781119820864/)

- [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/)

- [Building LLM Powered Applications](https://www.oreilly.com/library/view/building-llm-powered/9781835462317/)

- [Building LLMs for Production](https://www.oreilly.com/library/view/building-llms-for/9798324731472/)

- [Building Recommendation Systems in Python and JAX](https://www.oreilly.com/library/view/building-recommendation-systems/9781492097983/)

- [Conversational AI](https://www.oreilly.com/library/view/conversational-ai/9781617298837/)

- [Deep Learning for Natural Language Processing](https://www.oreilly.com/library/view/deep-learning-for/9781617295447/)

- [Deep Learning for Time Series Cookbook](https://www.oreilly.com/library/view/deep-learning-for/9781805129233/)

- [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/)

- [Deep Learning with PyTorch / Eli Stevens, Luca Antiga, and Thomas Viehmann](https://www.manning.com/books/deep-learning-with-pytorch)

- [Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models](https://www.oreilly.com/library/view/deep-reinforcement-learning/9798868802737/)

- [Explainable AI for Practitioners](https://www.oreilly.com/library/view/explainable-ai-for/9781098119126/)

- [Foundations of Deep Reinforcement Learning: Theory and Practice in Python](https://www.oreilly.com/library/view/foundations-of-deep/9780135172490/)

- [Generative Deep Learning, 2nd Edition](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/)

- [Hands-On Genetic Algorithms with Python - Second Edition](https://www.oreilly.com/library/view/hands-on-genetic-algorithms/9781805123798/)

- [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/)

- [Inside Deep Learning](https://www.oreilly.com/library/view/inside-deep-learning/9781617298639/)

- [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/)

- [Low-Code AI](https://www.oreilly.com/library/view/low-code-ai/9781098146818/)

- [Machine Learning Approach for Cloud Data Analytics in IoT](https://www.oreilly.com/library/view/machine-learning-approach/9781119785804/)

- [Machine Learning Engineering in Action](https://www.oreilly.com/library/view/machine-learning-engineering/9781617298714/)

- [Machine Learning with Python Cookbook, 2nd Edition](https://www.oreilly.com/library/view/machine-learning-with/9781098135713/)

- [Natural Language Processing with Transformers, Revised Edition](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/)

- [Practical Deep Learning at Scale with MLflow](https://www.oreilly.com/library/view/practical-deep-learning/9781803241333/)

- [Practical Natural Language Processing](https://www.oreilly.com/library/view/practical-natural-language/9781492054047/)

- [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/)

  

## ⛓️ ML & AI Frameworks

- [FLAX](https://flax.readthedocs.io/en/latest/)

- [JAX](https://jax.readthedocs.io/en/latest/)

- [Keras](https://keras.io)

- [MLX](https://ml-explore.github.io/mlx/build/html/install.html)

- [PyTorch](https://pytorch.org)

- [TensorFlow](https://www.tensorflow.org)

- [Micrograd](https://github.com/karpathy/micrograd)

- [Tinygrad](https://github.com/tinygrad/tinygrad)



## βš™οΈ SDKs and Development Tools

- [AutoGEN](https://microsoft.github.io/autogen/)

- [LangChain](https://www.langchain.com)

- [LlamaIndex](https://docs.llamaindex.ai/en/stable/)

- [Ollama](https://ollama.com)

- [vLLM](https://docs.vllm.ai/en/latest/)



## 🎞 WWDC24 | Apple

- [WWDC24: Bring your machine learning and AI models to Apple silicon | Apple](https://www.youtube.com/watch?v=ZgG2JVnJ7nw)

- [WWDC24: Deploy machine learning and AI models on-device with Core ML | Apple](https://www.youtube.com/watch?v=aawk4l9W9YU)

- [WWDC24: Explore machine learning on Apple platforms | Apple](https://www.youtube.com/watch?v=p_hyo2FRil4)

- [WWDC24: Support real-time ML inference on the CPU | Apple](https://www.youtube.com/watch?v=VdLgH2nJMBg)

- [WWDC24: Train your machine learning and AI models on Apple GPUs | Apple](https://www.youtube.com/watch?v=CbmTFTsbyPI)

- [WWDC24: What’s new in Create ML | Apple](https://www.youtube.com/watch?v=yjblfqwR37s)