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https://github.com/khushmeeet/ai-links

This is the list of links on Deep Learning that I have collected over time and still collecting.
https://github.com/khushmeeet/ai-links

List: ai-links

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This is the list of links on Deep Learning that I have collected over time and still collecting.

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# ai-links



This is the list of links on Deep Learning that I have collected over time.



## Resources



1. [An introduction to Generative Adversarial Networks (with code in TensorFlow) - AYLIEN News API](https://aylien.com/blog/introduction-generative-adversarial-networks-code-tensorflow)

2. [Generative Models](https://openai.com/blog/generative-models/)

3. [Adverarial Nets](https://cs.stanford.edu/people/karpathy/gan/)

4. [Generative Adversarial Nets in TensorFlow - Agustinus Kristiadi](https://agustinus.kristia.de/techblog/2016/09/17/gan-tensorflow/)

5. [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)

6. [Eric Jang: Generative Adversarial Nets in TensorFlow (Part I)](http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html)

7. [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)

8. [YouTube - Active One-shot Learning](https://www.youtube.com/watch?v=CzQSQ_0Z-QU)

9. [1605.06065 One-shot Learning with Memory-Augmented Neural Networks](https://arxiv.org/abs/1605.06065)

10. [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)

11. [Google’s DeepMind AI Now Capable of ‘Deep Neural Reasoning’ – The New Stack](https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/)

12. [Tutorial - What is a variational autoencoder? – Jaan Altosaar](https://jaan.io/what-is-variational-autoencoder-vae-tutorial/)

13. [Variational Autoencoders Explained](https://kvfrans.com/variational-autoencoders-explained/)

14. [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)

15. [Variational Autoencoder in TensorFlow](https://jmetzen.github.io/2015-11-27/vae.html)

16. [Eric Jang: Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax](https://blog.evjang.com/2016/11/tutorial-categorical-variational.html)

17. [Implementing Dynamic memory networks · YerevaNN](https://yerevann.github.io/2016/02/05/implementing-dynamic-memory-networks/)

18. [Variational Autoencoder (VAE) in Pytorch - Agustinus Kristiadi](https://agustinus.kristia.de/techblog/2017/01/24/vae-pytorch/)

19. [PyTorch quick start: Classifying an image — Outcome Blog  documentation](http://blog.outcome.io/pytorch-quick-start-classifying-an-image/)

20. [An end to end implementation of a Machine Learning pipelinet](https://spandan-madan.github.io/DeepLearningProject/)

21. [1602.05568 Multi-layer Representation Learning for Medical Concepts](https://arxiv.org/abs/1602.05568)

22. [1605.03481 Tweet2Vec: Character-Based Distributed Representations for Social Media](https://arxiv.org/abs/1605.03481)

23. [1603.07012 Semi-supervised Word Sense Disambiguation with Neural Models](https://arxiv.org/abs/1603.07012)

24. [1708.00524 Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://arxiv.org/abs/1708.00524)

25. [1704.08847 Parseval Networks: Improving Robustness to Adversarial Examples](https://arxiv.org/abs/1704.08847)

26. [SoundNet: Learning Sound Representations from Unlabeled Video - MIT](http://soundnet.csail.mit.edu/)

27. [DeepMoji](http://deepmoji.mit.edu/)

28. [Introduction to Machine Learning Interviews Book · MLIB](https://huyenchip.com/ml-interviews-book/)

29. [Fastcore - Fast.ai](https://fastcore.fast.ai)

30. [Explained AI](https://explained.ai)

31. [Schedule «  AGI-21: SF Bay Area and Virtual, Oct. 15-18, 2021](http://agi-conf.org/2021/schedule/?mc_cid=3042e76e83&mc_eid=ecc743345f)

32. [Machine Learning Crash Course  |  Google Developers](https://developers.google.com/machine-learning/crash-course)

33. [Stanford CRFM](https://crfm.stanford.edu/workshop.html)

34. [HuBERT: How to Apply BERT to Speech, Visually Explained | Jonathan Bgn](https://jonathanbgn.com/2021/10/30/hubert-visually-explained.html)

35. [Python Numpy Tutorial (with Jupyter and Colab)](https://cs231n.github.io/python-numpy-tutorial/)

36. [Stanford DAWN · DAWN](https://dawn.cs.stanford.edu)

37. [2022 AGI Safety Fundamentals alignment curriculum](https://docs.google.com/document/d/1mTm_sT2YQx3mRXQD6J2xD2QJG1c3kHyvX8kQc_IQ0ns/mobilebasic)

38. [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://socraticmodels.github.io/)

39. [Deep Learning Links](https://deep-learning-links.carrd.co/)

40. [MLExpert | MLExpert - land your dream Machine Learning job](https://mlexpert.io/)

41. [Cloudera Fast Forward Blog](https://blog.fastforwardlabs.com/)

42. [Design Patterns in Machine Learning Code and Systems](https://eugeneyan.com/writing/design-patterns/)

43. [The Illustrated Machine Learning Website](https://illustrated-machine-learning.github.io/index.html)



## Deep Learning Repositories



1. [GitHub - teddykoker/tinyloader](https://github.com/teddykoker/tinyloader)

2. [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)

3. [GitHub - Renovamen/flint: A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.](https://github.com/Renovamen/flint)

4. [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)

5. [GitHub - Renovamen/metallic: A clean, lightweight and modularized PyTorch meta-learning library.](https://github.com/Renovamen/metallic)

6. [GitHub - graph4ai/graph4nlp: Graph4nlp is the library for the easy use of Graph Neural Networks for NLP](https://github.com/graph4ai/graph4nlp)

7. [GitHub - maziarraissi/Applied-Deep-Learning: Applied Deep Learning](https://github.com/maziarraissi/Applied-Deep-Learning)

8. [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)

9. [GitHub - NVIDIA/DeepLearningExamples: Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples)

10. [GitHub - rossant/awesome-math: A curated list of awesome mathematics resources](https://github.com/rossant/awesome-math)

11. [GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.](https://github.com/eugeneyan/applied-ml)

12. [GitHub - tmabraham/awesome-fastai: A curated list of awesome fastai projects/blog posts/tutorials/etc.](https://github.com/tmabraham/awesome-fastai)

13. [GitHub - booknlp/booknlp: BookNLP, a natural language processing pipeline for books](https://github.com/booknlp/booknlp)

14. [GitHub - amitness/learning: Becoming better at data science every day](https://github.com/amitness/learning)

15. [GitHub - dair-ai/Transformers-Recipe: A quick recipe to learn all about Transformers](https://github.com/dair-ai/Transformers-Recipe)

16. [GitHub - minitorch/minitorch: The full minitorch student suite.](https://github.com/minitorch/minitorch)

17. [GitHub - rmcelreath/stat_rethinking_2022: Statistical Rethinking course winter 2022](https://github.com/rmcelreath/stat_rethinking_2022)

18. [GitHub - qdrant/awesome-metric-learning: 😎 A curated list of awesome practical Metric Learning and its applications](https://github.com/qdrant/awesome-metric-learning)

19. [GitHub - kurtispykes/Natural-Language-Processing: Curated articles and code on NLP](https://github.com/kurtispykes/Natural-Language-Processing)

20. [GitHub - kurtispykes/Deep-Learning: Curated articles and code on deep learning topics](https://github.com/kurtispykes/Deep-Learning)

21. [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)

22. [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)

23. [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)

24. [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)

25. [GitHub - carpedm20/MemN2N-tensorflow: “End-To-End Memory Networks” in Tensorflow](https://github.com/carpedm20/MemN2N-tensorflow)

26. [GitHub - ryankiros/skip-thoughts: Sent2Vec encoder and training code from the paper “Skip-Thought Vectors”](https://github.com/ryankiros/skip-thoughts)

27. [GitHub - btcsuite/btcd: An alternative full node bitcoin implementation written in Go (golang)](https://github.com/btcsuite/btcd)

28. [GitHub - speechbrain/speechbrain: A PyTorch-based Speech Toolkit](https://github.com/speechbrain/speechbrain)

29. [GitHub - The-AI-Summer/learn-deep-learning: AI Summer’s complete catalog of articles](https://github.com/The-AI-Summer/learn-deep-learning)

30. [GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.](https://github.com/eugeneyan/applied-ml)

31. [GitHub - dennybritz/deeplearning-papernotes: Summaries and notes on Deep Learning research papers](https://github.com/dennybritz/deeplearning-papernotes)

32. [GitHub - The-AI-Summer/learn-deep-learning: AI Summer’s complete catalog of articles](https://github.com/The-AI-Summer/learn-deep-learning)

33. [GitHub - Synthaze/EpyNN: Educational python for Neural Networks.](https://github.com/synthaze/epynn)

34. [GitHub - Nyandwi/machine_learning_complete: A comprehensive repository containing 30+ notebooks on learning machine learning!](https://github.com/Nyandwi/machine_learning_complete)

35. [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)

36. [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)

37. [GitHub - khuyentran1401/Data-science: Collection of useful data science topics along with code and articles](https://github.com/khuyentran1401/Data-science)

38. [GitHub - Ying1123/awesome-neural-symbolic: A list of awesome neural symbolic papers.](https://github.com/Ying1123/awesome-neural-symbolic)

39. [GitHub - CYHSM/awesome-neuro-ai-papers: Papers from the intersection of deep learning and neuroscience](https://github.com/CYHSM/awesome-neuro-ai-papers)

40. [GitHub - hollance/neural-engine: Everything we actually know about the Apple Neural Engine (ANE)](https://github.com/hollance/neural-engine)

41. [https://github.com/karpathy/minGPT](https://github.com/karpathy/minGPT)

42. [GitHub - NielsRogge/Transformers-Tutorials: This repository contains demos I made with the Transformers library by HuggingFace.](https://github.com/NielsRogge/Transformers-Tutorials)

43. [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)

44. [GitHub - karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.](https://github.com/karpathy/nanoGPT)

45. [GitHub - dair-ai/ML-Papers-Explained: Explanation to key concepts in ML](https://github.com/dair-ai/ML-Papers-Explained)

46. [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook)

47. [GitHub - dair-ai/Transformers-Recipe: 🧠 A study guide to learn about Transformers](https://github.com/dair-ai/Transformers-Recipe)



## Interpretability



1. [GitHub - slundberg/shap: A game theoretic approach to explain the output of any machine learning model.](https://github.com/slundberg/shap)

2. [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)

3. [GitHub - g8a9/ferret: A python package for benchmarking interpretability techniques.](https://github.com/g8a9/ferret)

4. [A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J)

5. [GitHub - neelnanda-io/TransformerLens](https://github.com/neelnanda-io/TransformerLens)



## Publications, Annotations and Visualizations



1. [A Mathematical Framework for Transformer Circuits](https://transformer-circuits.pub/2021/framework/index.html)

2. [labml.ai Annotated PyTorch Paper Implementations](https://nn.labml.ai)

3. [explained.ai](https://explained.ai)

4. [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)

5. [Transformers from Scratch](https://e2eml.school/transformers.html)

6. [Pandas Tutor - visualize Python pandas code](https://pandastutor.com)

7. [The Illustrated Retrieval Transformer – Jay Alammar – Visualizing machine learning one concept at a time.](http://jalammar.github.io/illustrated-retrieval-transformer/)

8. [You don’t know JAX](https://colinraffel.com/blog/you-don-t-know-jax.html)

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

10. [Differentiable Programming from Scratch – Max Slater – Computer Graphics, Programming, and Math](https://thenumb.at/Autodiff/)

11. [Logistic Regression](https://mlu-explain.github.io/logistic-regression/)

12. [The Illustrated Stable Diffusion – Jay Alammar – Visualizing machine learning one concept at a time.](https://jalammar.github.io/illustrated-stable-diffusion/)

13. [CS 221 ― Artificial Intelligence - Cheat Sheets](https://stanford.edu/~shervine/teaching/)

14. [Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning - PDF](https://www.cis.upenn.edu/~jean/math-deep.pdf)

15. [GPT in 60 Lines of NumPy | Jay Mody](https://jaykmody.com/blog/gpt-from-scratch/)

16. [Annotated S4](https://srush.github.io/annotated-s4/)

17. [Attention, Transformers, in Neural Network Large Language Models](http://bactra.org/notebooks/nn-attention-and-transformers.html)

18. [Transformer Circuits](https://transformer-circuits.pub/)

19. [Transformers from Scratch](https://blog.matdmiller.com/posts/2023-06-10_transformers/notebook.html)

20. [OpenAI Microscope](https://microscope.openai.com/models)

21. [A Visual Guide to Vision Transformers](https://blog.mdturp.ch/posts/2024-04-05-visual_guide_to_vision_transformer.html)

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

23. [The Illustrated AlphaFold](https://elanapearl.github.io/blog/2024/the-illustrated-alphafold/)

24. [A Visual Guide to Quantization](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization)



## Courses



1. [Natural Language Processing (NLP) for Semantic Search | Pinecone](https://www.pinecone.io/learn/nlp/)

2. [Syllabus for Mathematical Background for Machine Learning](https://canvas.cmu.edu/courses/603/assignments/syllabus)

3. [Linear Algebra | Mathematics | MIT OpenCourseWare](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/)

4. [Deep Learning for Natural Language Processing](https://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/)

5. [Stanford CS 224N | Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/)

6. [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)

7. [GitHub - AMAI-GmbH/AI-Expert-Roadmap: Roadmap to becoming an Artificial Intelligence Expert in 2022](https://github.com/AMAI-GmbH/AI-Expert-Roadmap)

8. [Deep Learning for Particle Physicists — Deep Learning for Particle Physicists](https://lewtun.github.io/dl4phys/intro.html)

9. [Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/)

10. [AMMI Geometric Deep Learning Course - Second Edition (2022) - YouTube](https://www.youtube.com/playlist?app=desktop&list=PLn2-dEmQeTfSLXW8yXP4q_Ii58wFdxb3C)

11. [GitHub - microsoft/AI-For-Beginners: 12 Weeks, 24 Lessons, AI for All!](https://github.com/microsoft/AI-For-Beginners)

12. [First Principles of Computer Vision - YouTube](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw/playlists?app=desktop)

13. [UNIGE 14x050 – Deep Learning](https://fleuret.org/dlc/#vm)

14. [Deep Learning Systems](https://dlsyscourse.org/)

15. [GitHub - karpathy/nn-zero-to-hero: Neural Networks: Zero to Hero](https://github.com/karpathy/nn-zero-to-hero)

16. [Home - Made With ML](https://madewithml.com/)

17. [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)

18. [Cornell CS4780 - Machine Learning for Intelligent Systems](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/)

19. [“Crash Course” - ML@B Blog Berkeley](https://ml.berkeley.edu/blog/tag/crash-course)

20. [Natural Language Processing Demystified](https://www.nlpdemystified.org/)

21. [Deep Learning Fundamentals - Lightning AI](https://lightning.ai/pages/courses/deep-learning-fundamentals/)

22. [TinyML and Efficient Deep Learning Computing](https://hanlab.mit.edu/courses/2023-fall-65940)

23. [GitHub - stas00/ml-engineering: Machine Learning Engineering Online Book](https://github.com/stas00/ml-engineering)

25. ![Courses](./courses.png)



## MLOps



1. [GitHub - dair-ai/MLOPs-Primer: A collection of resources to learn about MLOPs.](https://github.com/dair-ai/MLOPs-Primer)



## Industry Related



1. [GitHub - andrewekhalel/MLQuestions: Machine Learning and Computer Vision Engineer - Technical Interview Questions](https://github.com/andrewekhalel/MLQuestions)

2. [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)



## Contribute



To 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.