The Last 5 Years In Deep Learning
Introduction As we’re nearing the end of 2017 (and coincidentally the first day of NIPS 2017), we’ve come to the 5 year landmark of deep learning really starting to hit the mainstream. For me, I think of AlexNet and the 2012 Imagenet competition as the coming out party (although researchers have definitely been working... Read more
Top Deep Learning Projects
A list of popular github projects related to deep learning (ranked by stars automatically). Please update list.txt (via pull requests) Project Name Stars Description tensorflow 44154 Computation using data flow graphs for scalable machine learning caffe 15611 Caffe: a fast open framework for deep learning. neural-style 12491 Torch implementation... Read more
Facelyzr Deep Learning Project
Introduction     Deep learning is a phrase that follows us everywhere. Even the non-technical people are aware how artificial intelligence can change our world. I totally agree with Andrew Ng that “AI is the new electricity” and that it can make our world better. Especially that part where... Read more
Learning Deep Learning Series Part 1: Videos
Intro. How I Plan to Teach Myself Deep Learning Using Only Free Resources Learning Deep Learning Series Part 1: Videos Learning Deep Learning Part 2: Online Courses Learning Deep Learning Part 3: Github Repos Last week Open Data Science published an article about how I plan to teach myself... Read more
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