Deep Learning Guides, Tutorials, Research, and Use-Cases

Linear algebra cheat sheet for Deep Learning

Blog Deep Learning Deep Learningposted by Brendan Fortuner June 12, 2017

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

Blog Deep Learning Convolutional Neural Networks|Neural Networksposted by Adit Deshpande June 11, 2017

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

Blog Deep Learning Deep Learningposted by Oren Kraus June 8, 2017

Introduction Advances in automated image acquisition and analysis, coupled with the availability of reagents for genome‐scale perturbation, have enabled systematic analyses of cellular and subcellular phenotypes (Mattiazzi Usaj et al, 2016). One powerful application of microscopy‐based assays involves assessment of changes in the subcellular... Read more

A Beginner’s Guide To Understanding Convolutional Neural Networks

Blog Deep Learningposted by Adit Deshpande June 5, 2017

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

Blog Deep Learning NLP/Text Analytics Machine Learning|Neural Networks|Probabilistic Computationposted by Burak Himmetoglu May 31, 2017

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

Deep Learning Modeling Python Tools & Languages Machine Learning|Neural Networks|TensorFlowposted by Saimadhu Polamuri May 11, 2017

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

Deep Learning Modelingposted by Thomas Wiecki, PhD April 27, 2017

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

TensorFlow Clusters: Questions and Code

Deep Learning Modeling Platforms Tools & Languagesposted by Aaron Schumacher April 23, 2017

One way to think about TensorFlow is as a framework for distributed computing. I’ve suggested that TensorFlow is a distributed virtual machine. As such, it offers a lot of flexibility. TensorFlow also suggests some conventions that make writing programs for distributed computation tractable. When is there... Read more

Wading into Deep Learning, a 30 minute query

Deep Learning Modeling Deep Learning|Machine Learningposted by Diego Arenas, ODSC April 18, 2017

In this interview, Jonathan Schwarz of Google DeepMind shares insight on Deep Learning projects. He offers tips and advice for the those interested in DL, and explains whether DL projects relate to other data driven projects? He comments on effective team size, software, frameworks, common mistakes, resources for learning,... Read more

On word embeddings – Part 2: Approximating the Softmax

Deep Learning Modeling NLP/Text Analyticsposted by Sebastian Ruder April 17, 2017

Table of contents: Softmax-based Approaches Hierarchical Softmax Differentiated Softmax CNN-Softmax Sampling-based Approaches Importance Sampling Adaptive Importance Sampling Target Sampling Noise Contrastive Estimation Negative Sampling Self-Normalisation Infrequent Normalisation Other Approaches Which Approach to Choose? Conclusion This is the second post in a series on word embeddings and... Read more