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Links to blog posts about different topics in ML and AI.
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Machine Learning Blog Posts

Blog posts are some of the best resources for learning - here are some links to blog posts about different topics in ML and AI.

Contributions are welcome, just submit a pull request:

  • Blog posts only, no papers!
  • Follow the formatting conventions
  • Pull request and commit titles are useful
  • Try not to submit duplicates
  • Improvements to organization are welcome =D


Attention and Augmented Recurrent Neural Networks
Chris Olah, Shan Carter, Sep 2016

Attention in Neural Networks and How to Use It
Adam Kosiorek, Oct 2017

Attention? Attention!
Lilian Weng, Jun 2018

Convolutional neural networks (CNNs)

Conv Nets: A Modular Perspective
Chris Olah, Jul 2014

Understanding Convolutions
Chris Olah, Jul 2014

Dimensionality reduction

Visualizing MNIST: An Exploration of Dimensionality Reduction
Chris Olah, Oct 2014

How to Use t-SNE Effectively
Martin Wattenberg, Fernanda Viegas, Ian Johnson, Oct 2016

Generative adversarial networks (GANs)

InfoGAN: using the variational bound on mutual information (twice)
Ferenc Huszar, Aug 2016

Open Questions about Generative Adversarial Networks
Augustus Odena, Apr 2019

Gaussian processes

A Visual Exploration of Gaussian Processes
Jochen Gortler, Rebecca Kehlbeck, Oliver Deussen, Apr 2019

Information theory

Visual Information Theory
Chris Olah, Oct 2015


The Building Blocks of Interpretability
Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev, Mar 2018


Reflections on Random Kitchen Sinks
Ali Rahimi, Ben Recht, Dec 2017

Neural art

Differentiable Image Parameterizations
Alexander Mordvintsev, Nicola Pezzotti, Ludwig Schubert, Chris Olah, Jul 2018

Normalizing flows

Normalizing Flows Tutorial, Part 1: Distributions and Determinants
Eric Jang, Jan 2018

Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows
Eric Jang, Jan 2018

Normalizing Flows
Adam Kosiorek, Apr 2018

Change of Variables: A precursor to normalizing flow
Rui Shu, May 2018

Flow-based Deep Generative Models
Lilian Weng, Oct 2018


Why Momentum Really Works
Gabriel Goh, Apr 2017


Gaussian Distributions are Soap Bubbles
Ference Huszar, Nov 2017

Reinforcement learning (RL)

Deep Reinforcement Learning: Pong from Pixels
Andrej Karpathy, May 2016

AlphaGo, in context
Andrej Karpathy, May 2017

Recurrent neural networks (RNNs/LSTMs)

The Unreasonable Effectiveness of Recurrent Neural Networks
Andrej Karpathy, May 2015

Understanding LSTM Networks
Chris Olah, Aug 2015

Visualizing memorization in RNNs
Andreas Madsen, Mar 2019

Training neural networks

A Recipe for Training Neural Networks
Andrej Karpathy, Apr 2019

Variational autoencoders (VAEs)

A Tutorial on Information Maximizing Variational Autoencoders (InfoVAE)
Shengjia Zhao, Dec 2017

Density Estimation: Variational autoencoders
Rui Shu, Mar 2018

What is wrong with VAEs?
Adam Kosiorek, Mar 2018

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