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HACKER’S GUIDE TO NEURAL NETWORKS, #2

HACKER’S GUIDE TO ...

Chapter 2: Machine Learning In the last chapter we were concerned with real-valued circuits that computed possibly complex expressions of their inputs (the forward pass), and also we could compute the gradients of these expressions on the original inputs (backward pass). In this chapter we will see how useful this extremely simple mechanism is in […]

Hacker’s guide to Neural Networks, #1

Hacker’s guide...

Hi there, I’m a CS PhD student at Stanford. I’ve worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS – a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what’s going on and to play around with […]

Representation Learning Bonus Material

Representation Learn...

This post is part of a three part series. Notes on Representation Learning Notes on Representation Learning Continued Representation Learning Bonus Material Using GANs to Generate Images Based On Text Descriptions Below are some neat pictures demonstrating the use of GANs to generate images based on text descriptions.  All the images below are generated by a […]

Notes on Representation Learning Continued

Notes on Representat...

This post is part of a three part series. Notes on Representation Learning Notes on Representation Learning Continued Representation Learning Bonus Material Ten Shot Learning with Generative Adversarial Networks A very exciting approach to representation learning (but one that sadly does not work on discrete values like text, at least not without some modification) are Generative […]

Notes on Representation Learning

Notes on Representat...

TL;DR: Representation learning can eliminate the need for large labeled data sets to train deep neural networks, opening up new domains to machine learning and transforming the practice of Data Science. Check out “Notes on Representation Learning” in these three parts. Notes on Representation Learning Notes on Representation Learning Continued Representation Learning Bonus Material Deep Learning and […]

Nearest Neighbor Methods and Vector Models – part 1

Nearest Neighbor Met...

This is a blog post rewritten from a presentation at NYC Machine Learning. It covers a library called Annoy that I have built that helps you do (approximate) nearest neighbor queries in high dimensional spaces. I will be splitting it into several parts. This first talks about vector models, how to measure similarity, and why […]

Breaking Linear Classifiers on ImageNet

Breaking Linear Clas...

You’ve probably heard that Convolutional Networks work very well in practice and across a wide range of visual recognition problems. You may have also read articles and papers that claim to reach a near “human-level performance”. There are all kinds of caveats to that (e.g. see my G+ post on Human Accuracy is not a […]

A Quick Introduction to Neural Networks

A Quick Introduction...

An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this blog post we will try to […]

Attention and Memory in Deep Learning and NLP

Attention and Memory...

A recent trend in Deep Learning are Attention Mechanisms. In an interview, Ilya Sutskever, now the research director of OpenAI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay. That sounds exciting. But what are Attention Mechanisms? Attention Mechanisms in Neural Networks are (very) loosely based […]