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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 […]

An Intuitive Explanation of Convolutional Neural Networks

An Intuitive Explana...

Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. Figure 1: Source [1] In Figure 1 above, a ConvNet is able […]

RNNs in Tensorflow, a Practical Guide and Undocumented Features

RNNs in Tensorflow, ...

In a previous tutorial series I went over some of the theory behind Recurrent Neural Networks (RNNs) and the implementation of a simple RNN from scratch. That’s a useful exercise, but in practice we use libraries like Tensorflow with high-level primitives for dealing with RNNs. With that using an RNN should be as easy as […]