Deep Learning Research Review Week 2: Reinforcement Learning
This is the 2nd installment of 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 focuses on Reinforcement Learning. Last time was Generative Adversarial Networks ICYMI Introduction to Reinforcement Learning... Read more
Can neural networks solve any problem?
Visualizing the Universal Approximation Theorem At some point in your deep learning journey you probably came across the Universal Approximation Theorem. A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly.... Read more
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 is the coolest... 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 topics to improve... 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 in GFP‐tagged yeast... Read more
Deep Learning as the apotheosis of Test-Driven Development
Even if you aren’t interested in data science, Deep Learning is an interesting programming paradigm; you can see it as “doing test-driven development with a ludicrously large number of tests, an IDE that writes most of the code, and a forgiving client.” No wonder everybody’s pouring so much money... Read more
Amazon Enters The Open-Source Deep Learning Fray
The Synergy Research Group’s last report of 2015 attributed 31% of the cloud computing market to Amazon’s Amazon Web Services (AWS), nearly four times as much as its nearest competitor, Microsoft. This would come as no surprise to any programmer, Data Engineer, or Data Scientist, AWS is a mainstay... Read more