Jul 28, 2017
Taming wave functions with neural networks
The wave function is essential to most calculations in quantum mechanics, and yet it's a difficult beast to tame. Can neural networks help?
Feb 27, 2017
Differentiable memory and the brain
DeepMind's Differentiable Neural Computer (DNC) represents the state of the art in differentiable memory models. I introduce an analogy between the DNC and human memory, then discuss where it breaks down.
Jan 7, 2017
Learning the Enigma with Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are Turing-complete. In other words, they can approximate any function. As a tip of the hat to Alan Turing, let's see if we can use them to learn the Nazi Enigma.
Nov 26, 2016
A bird's eye view of synthetic gradients
Synthetic gradients achieve the perfect balance of crazy and brilliant. In a 100-line Gist I'll introduce this exotic technique and use it to train a neural network.
Sep 5, 2016
The art of regularization
Regularization seems fairly insignificant at first glance, but it has a huge impact on deep models. I'll use a one-layer neural network trained on the MNIST dataset to give an intuition for how common regularization techniques affect learning.
Aug 21, 2016
Scribe: realistic handwriting with TensorFlow
In this post, I will demonstrate the power of deep learning by using it to generate human-like handwriting. This work is based on Generating Sequences With Recurrent Neural Networks by Alex Graves
Aug 5, 2016
What is deep learning?
After being obsessed with this field for more than a year, I should have a concise and satisfying answer. Strangely, I have three.