• Deep Reinforcement Learning: Pong from Pixels
    I'll discuss the core ideas, pros and cons of policy gradients, a standard approach to the rapidly growing and exciting area of deep reinforcement learning. As a running example we'll learn to play ATARI 2600 Pong from raw pixels.
  • Short Story on AI: A Cognitive Discontinuity.
    The first part of a short story collection that has been on my mind for a long while. Exciting! :)
  • What a Deep Neural Network thinks about your #selfie
    We will look at Convolutional Neural Networks, with a fun example of training them to classify #selfies as good/bad based on a scraped dataset of 2 million selfies.
  • The Unreasonable Effectiveness of Recurrent Neural Networks
    We'll train and sample from character-level RNN language models that learn to write poetry, latex math and code. We'll also analyze the models and get hints of future research directions.
  • Breaking Linear Classifiers on ImageNet
    There have been a few recent papers that fool ConvNets by taking a correctly classified image and perturbing it in an imperceptible way to produce an image that is misclassified. In this post I show that ConvNets are an overkill: Simple linear classifiers are in fact susceptible to the same fooling strategy.
  • What I learned from competing against a ConvNet on ImageNet
    The latest state of the art Image Classification networks have only 6.7% Hit@5 error on ILSVRC 2014 classification task. How do humans compare?
  • Quantifying Productivity
    Describing a new pet project that tracks active windows and keystroke frequencies over the duration of a day (on Ubuntu/OSX) and creates pretty HTML visualizations of the data. This allows me to gain nice insights into my productivity. Code on Github.
  • Feature Learning Escapades
    Some reflections on the last two years of my research: The Quest for Unsupervised Feature Learning algorithms for visual data. Where it was, where it is, and where it's going. Maybe.
  • Visualizing Top Tweeps with t-SNE, in Javascript
    A writeup of a recent mini-project: I scraped tweets of the top 500 Twitter accounts and used t-SNE to visualize the accounts so that people who tweet similar things are nearby. My final Javascript implementation of t-SNE is released on Github as tsnejs.
  • Switching Blog from Wordpress to Jekyll
    I can't believe I lasted this long on Wordpress. I am switching permanently to Jekyll for hosting my blog, and so should you :) Details inside.
  • Interview with Data Science Weekly on Neural Nets and ConvNetJS
    I gave a (long) interview about my background and perspectives on neural nets.
  • Quantifying Hacker News with 50 days of data
    I scraped Hacker News Front Page and New Page every minute for 50 days and analyzed the results. How do stories rise and fall on Hacker News? What makes a successful post? Find out in this post :)
  • Chrome Extension Programming: Illustrating a Basic Survival Skill with a Twitter Case Study
    I illustrate a very valuable skill (Chrome Extension Programming) using a Twitter Case study. We will give Twitter a face lift, get it to refresh new tweets automatically, and highlight tweets from people who rarely tweet. All with a few lines of Javascript!
  • The state of Computer Vision and AI: we are really, really far away.
    A depressing look at the state of Computer Vision Research and AI in general. For those who like to think that AI is anywhere close.
  • Lessons learned from manually classifying CIFAR-10
    CIFAR-10 is a popular dataset small dataset for testing out Computer Vision Deep Learning learning methods. We're seeing a lot of improvements. But what is the human baseline?