fbpx
Deep Learning Research Review Week 1: Generative Adversarial Nets
This week, I’ll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week I’ll begin with Generative Adversarial Networks.  Introduction According to Yann LeCun, “adversarial training... Read more
In part two of my XKCD font saga I was able to separate strokes from the XKCD handwriting dataset into many smaller images. I also handled the easier cases of merging some of the strokes back together – I particularly focussed on “dotty” or “liney” type... Read more
An Introduction to Deep Learning using nolearn
NOTE: If you are having trouble with nolearn working properly, make sure you are using version 0.5b1 available here. Otherwise you may run into problems. One of the most well known problems in machine learning regards how to categorize handwritten numbers automatically. Basically, the idea is... Read more
Linear algebra cheat sheet for Deep Learning
Beginner’s guide to commonly used operations During Jeremy Howard’s excellent deep learning course I realized I was a little rusty on the prerequisites and my fuzziness was impacting my ability to understand concepts like backpropagation. I decided to put together a few wiki pages on these... Read more
A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2
Introduction Link to Part 1 In this post, we’ll go into a lot more of the specifics of ConvNets. Disclaimer: Now, I do realize that some of these topics are quite complex and could be made in whole posts by themselves. In an effort to remain... Read more
Automated analysis of High‐content Microscopy data with Deep Learning
    Deep learning is used to classify protein subcellular localization in genome‐wide microscopy screens of GFP‐tagged yeast strains. The resulting classifier (DeepLoc) outperforms previous classification methods and is transferable across image sets. A deep convolutional neural network (DeepLoc) is trained to classify protein subcellular localization... Read more
A Beginner’s Guide To Understanding Convolutional Neural Networks
Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to... Read more
Deciphering the Neural Language Model
Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student... Read more
Handwritten digits recognition using Tensorflow with Python
The progress in technology that has happened over the last 10 years is unbelievable. Every corner of the world is using the top most technologies to improve existing products while also conducting immense research into inventing products that make the world the best place to live.... Read more
Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary Data
Thomas originally posted this article here at http://twiecki.github.io  Most problems solved by Deep Learning are stationary. A cat is always a cat. The rules of Go have remained stable for 2,500 years, and will likely stay that way. However, what if the world around you is changing? This... Read more