Read List

  • A curated set of websites/blogs I follow to get my latest scoop in AI/ML.

Serious Reading

  • A modern medium for presenting research that showcases AI/ML concepts in clear, dynamic and vivid form.

Christopher Olah’s Blog

  • OpenAI machine learning researcher who likes to understand things clearly, and explain them well.
  • Author of the wildly popular “Understanding LSTM Networks” post.

Lilian Weng’s Blog

  • Robotics researcher @ OpenAI documenting her learning notes.

Andrej Karpathy’s Blog

Jay Alammar’s Blog

Kevin Zakka’s Blog

  • First-year Computer Science Master’s student at Stanford University writes on his experiences with AI/ML.

Awesome Deep Learning

Awesome Deep Vision

  • A curated list of deep learning resources for computer vision.

Awesome NLP

  • A curated list of resources dedicated to NLP.

Awesome GPT-3

  • A group of (awesome) demos and articles about the OpenAI GPT-3 API.

Course Notes

Stanford CS231n Notes

  • Notes that accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition.

Stanford CS224n Notes

  • Notes that accompany the Stanford CS class CS224n: Natural Language Processing with Deep Learning.

Stanford CS230 Section Notes

  • Notes that accompany the Stanford CS class CS230 Deep Learning.

Stanford CS229 Notes

  • Notes that accompany the Stanford CS class CS229 Machine Learning.

Stanford CS131 Notes

  • Notes that accompany the Stanford CS class CS131 Computer Vision: Foundations and Applications. Github with TeX source.

MIT Lecture Notes on Artificial Intelligence

  • Notes that accompany MIT’s 6.034 Artificial Intelligence.


Attention Is All You Need by Vaswani et al.
  • The paper from NIPS 2017 that introduced Transformers which are prevalent in most NLP and CV tasks today.
Deep Residual Learning for Image Recognition by He et al.
  • ResNet paper from CVPR 2016. Most cited in several AI fields.
ImageNet Classification with Deep Convolutional Neural Networks by Krizhevsky et al.
  • The original AlexNet paper from NIPS 2012 that started it all.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network by Ledig et al.
  • Paper from CVPR 2017 that applies GANs for image super-resolution.
What Every Computer Scientist Should Know About Floating-Point Arithmetic by Goldberg et al.
  • Helps demystify your errors and enables you to write more careful code.
What Every Programmer Should Know About Memory by Drepper.
  • To help you understand how system memory works.

Light Refresher

Adit Deshpande’s Blog

  • UCLA CS ‘19 grad writes on AI/ML.

Lavanya’s Blog


Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

  • Intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron

  • Gain an intuitive understanding of the concepts and tools for building intelligent systems using a range of techniques, starting with simple linear regression and progressing to deep neural networks.

Pattern Recognition and Machine Learning by Christopher Bishop

  • First textbook on pattern recognition to present approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

The Machine Learning Engineering Book by Andriy Burkov

  • For data analysts who lean towards a machine learning engineering role, and machine learning engineers alike who want to bring more structure to their work.

The Hundred-Page Machine Learning Book by Andriy Burkov

  • All you need to know about Machine Learning in a hundred pages.


PapersWithCode State-of-the-Art

  • The latest AI/ML papers, with code and leaderboards comparing implementations in several Computer Vision and NLP sub-tasks.

AI Conference Deadlines

  • Countdowns to top CV/NLP/ML/Robotics/AI conference deadlines.

Acceptance rate for major AI conferences

  • Statistics of acceptance rate for the major AI conferences.