{"id":58228,"url":"https://github.com/khushmeeet/ai-links","name":"ai-links","description":"This is the list of links on Deep Learning that I have collected over time and still collecting.","projects_count":148,"last_synced_at":"2026-10-02T19:00:35.322Z","repository":{"id":52490554,"uuid":"509352084","full_name":"khushmeeet/ai-links","owner":"khushmeeet","description":"This is the list of links on Deep Learning that I have collected over time and still collecting.","archived":false,"fork":false,"pushed_at":"2025-11-01T07:32:37.000Z","size":640,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-08-04T15:03:33.831Z","etag":null,"topics":["awesome-list","deep-learning","machine-learning"],"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/khushmeeet.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}},"created_at":"2022-07-01T06:56:10.000Z","updated_at":"2025-11-01T07:32:40.000Z","dependencies_parsed_at":"2024-08-14T10:15:24.499Z","dependency_job_id":"e598b5da-734a-424e-8ab5-b86f072b6995","html_url":"https://github.com/khushmeeet/ai-links","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/khushmeeet/ai-links","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/khushmeeet%2Fai-links","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/khushmeeet%2Fai-links/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/khushmeeet%2Fai-links/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/khushmeeet%2Fai-links/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/khushmeeet","download_url":"https://codeload.github.com/khushmeeet/ai-links/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/khushmeeet%2Fai-links/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":341189360,"owners_count":37249200,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-22T15:14:58.755Z","status":"online","status_checked_at":"2026-09-12T02:00:07.213Z","response_time":57,"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"}},"created_at":"2024-04-08T00:00:29.458Z","updated_at":"2026-10-02T19:00:35.323Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Deep Learning Repositories","Resources","Courses","Publications, Annotations and Visualizations","Interpretability","Publications and Annotations","Industry Related","MLOps"],"sub_categories":[],"readme":"# ai-links\n\nThis is the list of links on Deep Learning that I have collected over time.\n\n## Resources\n\n1. [An introduction to Generative Adversarial Networks (with code in TensorFlow) - AYLIEN News API](https://aylien.com/blog/introduction-generative-adversarial-networks-code-tensorflow)\n2. [Generative Models](https://openai.com/blog/generative-models/)\n3. [Adverarial Nets](https://cs.stanford.edu/people/karpathy/gan/)\n4. [Generative Adversarial Nets in TensorFlow - Agustinus Kristiadi](https://agustinus.kristia.de/techblog/2016/09/17/gan-tensorflow/)\n5. [A (Very) Gentle Introduction to Generative Adversarial Networks (a.k…](https://www.slideshare.net/ThomasDaSilvaPaula/a-very-gentle-introduction-to-generative-adversarial-networks-aka-gans-71614428)\n6. [Eric Jang: Generative Adversarial Nets in TensorFlow (Part I)](http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html)\n7. [Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode | by Arthur Juliani | Medium](https://awjuliani.medium.com/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39)\n8. [YouTube - Active One-shot Learning](https://www.youtube.com/watch?v=CzQSQ_0Z-QU)\n9. [1605.06065 One-shot Learning with Memory-Augmented Neural Networks](https://arxiv.org/abs/1605.06065)\n10. [Differential neural computer from DeepMind and more advances in backward propagation](https://futuristech.info/posts/differential-neural-computer-from-deepmind-and-more-advances-in-backward-propagation)\n11. [Google’s DeepMind AI Now Capable of ‘Deep Neural Reasoning’ – The New Stack](https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/)\n12. [Tutorial - What is a variational autoencoder? – Jaan Altosaar](https://jaan.io/what-is-variational-autoencoder-vae-tutorial/)\n13. [Variational Autoencoders Explained](https://kvfrans.com/variational-autoencoders-explained/)\n14. [Under the Hood of the Variational Autoencoder (in Prose and Code)](https://blog.fastforwardlabs.com/2016/08/22/under-the-hood-of-the-variational-autoencoder-in-prose-and-code.html)\n15. [Variational Autoencoder in TensorFlow](https://jmetzen.github.io/2015-11-27/vae.html)\n16. [Eric Jang: Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax](https://blog.evjang.com/2016/11/tutorial-categorical-variational.html)\n17. [Implementing Dynamic memory networks · YerevaNN](https://yerevann.github.io/2016/02/05/implementing-dynamic-memory-networks/)\n18. [Variational Autoencoder (VAE) in Pytorch - Agustinus Kristiadi](https://agustinus.kristia.de/techblog/2017/01/24/vae-pytorch/)\n19. [PyTorch quick start: Classifying an image — Outcome Blog  documentation](http://blog.outcome.io/pytorch-quick-start-classifying-an-image/)\n20. [An end to end implementation of a Machine Learning pipelinet](https://spandan-madan.github.io/DeepLearningProject/)\n21. [1602.05568 Multi-layer Representation Learning for Medical Concepts](https://arxiv.org/abs/1602.05568)\n22. [1605.03481 Tweet2Vec: Character-Based Distributed Representations for Social Media](https://arxiv.org/abs/1605.03481)\n23. [1603.07012 Semi-supervised Word Sense Disambiguation with Neural Models](https://arxiv.org/abs/1603.07012)\n24. [1708.00524 Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://arxiv.org/abs/1708.00524)\n25. [1704.08847 Parseval Networks: Improving Robustness to Adversarial Examples](https://arxiv.org/abs/1704.08847)\n26. [SoundNet: Learning Sound Representations from Unlabeled Video - MIT](http://soundnet.csail.mit.edu/)\n27. [DeepMoji](http://deepmoji.mit.edu/)\n28. [Introduction to Machine Learning Interviews Book · MLIB](https://huyenchip.com/ml-interviews-book/)\n29. [Fastcore - Fast.ai](https://fastcore.fast.ai)\n30. [Explained AI](https://explained.ai)\n31. [Schedule «  AGI-21: SF Bay Area and Virtual, Oct. 15-18, 2021](http://agi-conf.org/2021/schedule/?mc_cid=3042e76e83\u0026mc_eid=ecc743345f)\n32. [Machine Learning Crash Course  |  Google Developers](https://developers.google.com/machine-learning/crash-course)\n33. [Stanford CRFM](https://crfm.stanford.edu/workshop.html)\n34. [HuBERT: How to Apply BERT to Speech, Visually Explained | Jonathan Bgn](https://jonathanbgn.com/2021/10/30/hubert-visually-explained.html)\n35. [Python Numpy Tutorial (with Jupyter and Colab)](https://cs231n.github.io/python-numpy-tutorial/)\n36. [Stanford DAWN · DAWN](https://dawn.cs.stanford.edu)\n37. [2022 AGI Safety Fundamentals alignment curriculum](https://docs.google.com/document/d/1mTm_sT2YQx3mRXQD6J2xD2QJG1c3kHyvX8kQc_IQ0ns/mobilebasic)\n38. [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://socraticmodels.github.io/)\n39. [Deep Learning Links](https://deep-learning-links.carrd.co/)\n40. [MLExpert | MLExpert - land your dream Machine Learning job](https://mlexpert.io/)\n41. [Cloudera Fast Forward Blog](https://blog.fastforwardlabs.com/)\n42. [Design Patterns in Machine Learning Code and Systems](https://eugeneyan.com/writing/design-patterns/)\n43. [The Illustrated Machine Learning Website](https://illustrated-machine-learning.github.io/index.html)\n\n## Deep Learning Repositories\n\n1. [GitHub - teddykoker/tinyloader](https://github.com/teddykoker/tinyloader)\n2. [GitHub - karpathy/micrograd: A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API](https://github.com/karpathy/micrograd)\n3. [GitHub - Renovamen/flint: A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.](https://github.com/Renovamen/flint)\n4. [GitHub - Renovamen/Text-Classification: PyTorch implementation of some text classification models (HAN, fastText, BiLSTM-Attention, TextCNN, Transformer) | 文本分类](https://github.com/Renovamen/Text-Classification)\n5. [GitHub - Renovamen/metallic: A clean, lightweight and modularized PyTorch meta-learning library.](https://github.com/Renovamen/metallic)\n6. [GitHub - graph4ai/graph4nlp: Graph4nlp is the library for the easy use of Graph Neural Networks for NLP](https://github.com/graph4ai/graph4nlp)\n7. [GitHub - maziarraissi/Applied-Deep-Learning: Applied Deep Learning](https://github.com/maziarraissi/Applied-Deep-Learning)\n8. [GitHub - dair-ai/ML-YouTube-Courses: A repository to index and organize the latest machine learning courses found on YouTube.](https://github.com/dair-ai/ML-YouTube-Courses)\n9. [GitHub - NVIDIA/DeepLearningExamples: Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples)\n10. [GitHub - rossant/awesome-math: A curated list of awesome mathematics resources](https://github.com/rossant/awesome-math)\n11. [GitHub - eugeneyan/applied-ml: 📚 Papers \u0026 tech blogs by companies sharing their work on data science \u0026 machine learning in production.](https://github.com/eugeneyan/applied-ml)\n12. [GitHub - tmabraham/awesome-fastai: A curated list of awesome fastai projects/blog posts/tutorials/etc.](https://github.com/tmabraham/awesome-fastai)\n13. [GitHub - booknlp/booknlp: BookNLP, a natural language processing pipeline for books](https://github.com/booknlp/booknlp)\n14. [GitHub - amitness/learning: Becoming better at data science every day](https://github.com/amitness/learning)\n15. [GitHub - dair-ai/Transformers-Recipe: A quick recipe to learn all about Transformers](https://github.com/dair-ai/Transformers-Recipe)\n16. [GitHub - minitorch/minitorch: The full minitorch student suite.](https://github.com/minitorch/minitorch)\n17. [GitHub - rmcelreath/stat_rethinking_2022: Statistical Rethinking course winter 2022](https://github.com/rmcelreath/stat_rethinking_2022)\n18. [GitHub - qdrant/awesome-metric-learning: 😎 A curated list of awesome practical Metric Learning and its applications](https://github.com/qdrant/awesome-metric-learning)\n19. [GitHub - kurtispykes/Natural-Language-Processing: Curated articles and code on NLP](https://github.com/kurtispykes/Natural-Language-Processing)\n20. [GitHub - kurtispykes/Deep-Learning: Curated articles and code on deep learning topics](https://github.com/kurtispykes/Deep-Learning)\n21. [GitHub - lucidrains/DALLE2-pytorch: Implementation of DALL-E 2, OpenAI’s updated text-to-image synthesis neural network,  in Pytorch](https://github.com/lucidrains/DALLE2-pytorch)\n22. [GitHub - ivan-bilan/The-NLP-Pandect: A comprehensive reference for all topics related to Natural Language Processing](https://github.com/ivan-bilan/The-NLP-Pandect)\n23. [GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.](https://github.com/ritchieng/the-incredible-pytorch)\n24. [GitHub - anantzoid/VQA-Keras-Visual-Question-Answering: Visual Question Answering task written in Keras that answers questions about images](https://github.com/anantzoid/VQA-Keras-Visual-Question-Answering)\n25. [GitHub - carpedm20/MemN2N-tensorflow: “End-To-End Memory Networks” in Tensorflow](https://github.com/carpedm20/MemN2N-tensorflow)\n26. [GitHub - ryankiros/skip-thoughts: Sent2Vec encoder and training code from the paper “Skip-Thought Vectors”](https://github.com/ryankiros/skip-thoughts)\n27. [GitHub - btcsuite/btcd: An alternative full node bitcoin implementation written in Go (golang)](https://github.com/btcsuite/btcd)\n28. [GitHub - speechbrain/speechbrain: A PyTorch-based Speech Toolkit](https://github.com/speechbrain/speechbrain)\n29. [GitHub - The-AI-Summer/learn-deep-learning: AI Summer’s complete catalog of articles](https://github.com/The-AI-Summer/learn-deep-learning)\n30. [GitHub - eugeneyan/applied-ml: 📚 Papers \u0026 tech blogs by companies sharing their work on data science \u0026 machine learning in production.](https://github.com/eugeneyan/applied-ml)\n31. [GitHub - dennybritz/deeplearning-papernotes: Summaries and notes on Deep Learning research papers](https://github.com/dennybritz/deeplearning-papernotes)\n32. [GitHub - The-AI-Summer/learn-deep-learning: AI Summer’s complete catalog of articles](https://github.com/The-AI-Summer/learn-deep-learning)\n33. [GitHub - Synthaze/EpyNN: Educational python for Neural Networks.](https://github.com/synthaze/epynn)\n34. [GitHub - Nyandwi/machine_learning_complete: A comprehensive repository containing 30+ notebooks on learning machine learning!](https://github.com/Nyandwi/machine_learning_complete)\n35. [GitHub - kenjihiranabe/The-Art-of-Linear-Algebra: Graphic notes on Gilbert Strang’s “Linear Algebra for Everyone”](https://github.com/kenjihiranabe/The-Art-of-Linear-Algebra)\n36. [GitHub - dair-ai/ML-Notebooks: A series of code examples for all sorts of machine learning tasks and applications.](https://github.com/dair-ai/ML-Notebooks)\n37. [GitHub - khuyentran1401/Data-science: Collection of useful data science topics along with code and articles](https://github.com/khuyentran1401/Data-science)\n38. [GitHub - Ying1123/awesome-neural-symbolic: A list of awesome neural symbolic papers.](https://github.com/Ying1123/awesome-neural-symbolic)\n39. [GitHub - CYHSM/awesome-neuro-ai-papers: Papers from the intersection of deep learning and neuroscience](https://github.com/CYHSM/awesome-neuro-ai-papers)\n40. [GitHub - hollance/neural-engine: Everything we actually know about the Apple Neural Engine (ANE)](https://github.com/hollance/neural-engine)\n41. [https://github.com/karpathy/minGPT](https://github.com/karpathy/minGPT)\n42. [GitHub - NielsRogge/Transformers-Tutorials: This repository contains demos I made with the Transformers library by HuggingFace.](https://github.com/NielsRogge/Transformers-Tutorials)\n43. [GitHub - louisfb01/best_AI_papers_2022: A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.](https://github.com/louisfb01/best_AI_papers_2022)\n44. [GitHub - karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.](https://github.com/karpathy/nanoGPT)\n45. [GitHub - dair-ai/ML-Papers-Explained: Explanation to key concepts in ML](https://github.com/dair-ai/ML-Papers-Explained)\n46. [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook)\n47. [GitHub - dair-ai/Transformers-Recipe: 🧠 A study guide to learn about Transformers](https://github.com/dair-ai/Transformers-Recipe)\n\n## Interpretability\n\n1. [GitHub - slundberg/shap: A game theoretic approach to explain the output of any machine learning model.](https://github.com/slundberg/shap)\n2. [GitHub - cdpierse/transformers-interpret: Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.](https://github.com/cdpierse/transformers-interpret)\n3. [GitHub - g8a9/ferret: A python package for benchmarking interpretability techniques.](https://github.com/g8a9/ferret)\n4. [A Comprehensive Mechanistic Interpretability Explainer \u0026 Glossary - Dynalist](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J)\n5. [GitHub - neelnanda-io/TransformerLens](https://github.com/neelnanda-io/TransformerLens)\n\n## Publications, Annotations and Visualizations\n\n1. [A Mathematical Framework for Transformer Circuits](https://transformer-circuits.pub/2021/framework/index.html)\n2. [labml.ai Annotated PyTorch Paper Implementations](https://nn.labml.ai)\n3. [explained.ai](https://explained.ai)\n4. [GitHub - labmlai/annotated_deep_learning_paper_implementations: 🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, …), optimizers (Adam, adabelief, …), gans(cyclegan, stylegan2, …), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, … 🧠](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n5. [Transformers from Scratch](https://e2eml.school/transformers.html)\n6. [Pandas Tutor - visualize Python pandas code](https://pandastutor.com)\n7. [The Illustrated Retrieval Transformer – Jay Alammar – Visualizing machine learning one concept at a time.](http://jalammar.github.io/illustrated-retrieval-transformer/)\n8. [You don’t know JAX](https://colinraffel.com/blog/you-don-t-know-jax.html)\n9. [The Annotated Transformer](http://nlp.seas.harvard.edu/annotated-transformer/)\n10. [Differentiable Programming from Scratch – Max Slater – Computer Graphics, Programming, and Math](https://thenumb.at/Autodiff/)\n11. [Logistic Regression](https://mlu-explain.github.io/logistic-regression/)\n12. [The Illustrated Stable Diffusion – Jay Alammar – Visualizing machine learning one concept at a time.](https://jalammar.github.io/illustrated-stable-diffusion/)\n13. [CS 221 ― Artificial Intelligence - Cheat Sheets](https://stanford.edu/~shervine/teaching/)\n14. [Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning - PDF](https://www.cis.upenn.edu/~jean/math-deep.pdf)\n15. [GPT in 60 Lines of NumPy | Jay Mody](https://jaykmody.com/blog/gpt-from-scratch/)\n16. [Annotated S4](https://srush.github.io/annotated-s4/)\n17. [Attention, Transformers, in Neural Network Large Language Models](http://bactra.org/notebooks/nn-attention-and-transformers.html)\n18. [Transformer Circuits](https://transformer-circuits.pub/)\n19. [Transformers from Scratch](https://blog.matdmiller.com/posts/2023-06-10_transformers/notebook.html)\n20. [OpenAI Microscope](https://microscope.openai.com/models)\n21. [A Visual Guide to Vision Transformers](https://blog.mdturp.ch/posts/2024-04-05-visual_guide_to_vision_transformer.html)\n22. [LLM Visualization](https://bbycroft.net/llm)\n23. [The Illustrated AlphaFold](https://elanapearl.github.io/blog/2024/the-illustrated-alphafold/)\n24. [A Visual Guide to Quantization](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization)\n25. [LLM Visualization](https://bbycroft.net/llm)\n26. [Interactive Tools for machine learning, deep learning, and math](https://github.com/Machine-Learning-Tokyo/Interactive_Tools)\n27. [Smol Training Playbook](https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook#introduction)\n\n## Courses\n\n1. [Natural Language Processing (NLP) for Semantic Search | Pinecone](https://www.pinecone.io/learn/nlp/)\n2. [Syllabus for Mathematical Background for Machine Learning](https://canvas.cmu.edu/courses/603/assignments/syllabus)\n3. [Linear Algebra | Mathematics | MIT OpenCourseWare](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/)\n4. [Deep Learning for Natural Language Processing](https://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/)\n5. [Stanford CS 224N | Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/)\n6. [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n7. [GitHub - AMAI-GmbH/AI-Expert-Roadmap: Roadmap to becoming an Artificial Intelligence Expert in 2022](https://github.com/AMAI-GmbH/AI-Expert-Roadmap)\n8. [Deep Learning for Particle Physicists — Deep Learning for Particle Physicists](https://lewtun.github.io/dl4phys/intro.html)\n9. [Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/)\n10. [AMMI Geometric Deep Learning Course - Second Edition (2022) - YouTube](https://www.youtube.com/playlist?app=desktop\u0026list=PLn2-dEmQeTfSLXW8yXP4q_Ii58wFdxb3C)\n11. [GitHub - microsoft/AI-For-Beginners: 12 Weeks, 24 Lessons, AI for All!](https://github.com/microsoft/AI-For-Beginners)\n12. [First Principles of Computer Vision - YouTube](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw/playlists?app=desktop)\n13. [UNIGE 14x050 – Deep Learning](https://fleuret.org/dlc/#vm)\n14. [Deep Learning Systems](https://dlsyscourse.org/)\n15. [GitHub - karpathy/nn-zero-to-hero: Neural Networks: Zero to Hero](https://github.com/karpathy/nn-zero-to-hero)\n16. [Home - Made With ML](https://madewithml.com/)\n17. [GitHub - full-stack-deep-learning/fsdl-text-recognizer-2022-labs: Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022](https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022-labs)\n18. [Cornell CS4780 - Machine Learning for Intelligent Systems](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/)\n19. [“Crash Course” - ML@B Blog Berkeley](https://ml.berkeley.edu/blog/tag/crash-course)\n20. [Natural Language Processing Demystified](https://www.nlpdemystified.org/)\n21. [Deep Learning Fundamentals - Lightning AI](https://lightning.ai/pages/courses/deep-learning-fundamentals/)\n22. [TinyML and Efficient Deep Learning Computing](https://hanlab.mit.edu/courses/2023-fall-65940)\n23. [GitHub - stas00/ml-engineering: Machine Learning Engineering Online Book](https://github.com/stas00/ml-engineering)\n25. ![Courses](./courses.png)\n\n## MLOps\n\n1. [GitHub - dair-ai/MLOPs-Primer: A collection of resources to learn about MLOPs.](https://github.com/dair-ai/MLOPs-Primer)\n\n## Industry Related\n\n1. [GitHub - andrewekhalel/MLQuestions: Machine Learning and Computer Vision Engineer - Technical Interview Questions](https://github.com/andrewekhalel/MLQuestions)\n2. [GitHub - BoltzmannEntropy/interviews.ai: It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.](https://github.com/BoltzmannEntropy/interviews.ai)\n\n## Contribute\n\nTo add links to this repository that you think are in any of the given category, please raise a PR or an issue will suffice as well.\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/khushmeeet%2Fai-links/projects"}