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Deep Learning Framework

Deep Learning Framework

http://caffe.berkeleyvision.org/

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Hacker's guide to Neural Networks Hi there, I’m a CS PhD student at Stanford. I’ve worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what’s going on and to play around with the various hyperparameter settings, but I still regularly hear from people who ask for a more thorough treatment of the topic. This article (which I plan to slowly expand out to lengths of a few book chapters) is my humble attempt. It’s on web instead of PDF because all books should be, and eventually it will hopefully include animations/demos etc. My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code.

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Understanding Convolution in Deep Learning Convolution is probably the most important concept in deep learning right now. It was convolution and convolutional nets that catapulted deep learning to the forefront of almost any machine learning task there is. But what makes convolution so powerful? How does it work? In this blog post I will explain convolution and relate it to other concepts that will help you to understand convolution thoroughly. There are already some blog post regarding convolution in deep learning, but I found all of them highly confusing with unnecessary mathematical details that do not further the understanding in any meaningful way.

Best Machine Learning Resources for Getting Started This was a really hard post to write because I want it to be really valuable. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonised over what to include and what to exclude. I had to work hard to put my self in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them. I picked the best for each type of resource. The Numerical and Insightful Blog: A Gentle Introduction to Backpropagation Why is this blog being written? Neural networks have always fascinated me ever since I became aware of them in the 1990s. They are often represented with a hypnotizing array of connections. In the last decade, deep neural networks have dominated pattern recognition, often replacing other algorithms for applications like computer vision and voice recognition.

Google open-sources its software for making trippy images with deep learning Google today announced that it has open-sourced its code for generating wild and crazy images using a type of artificial intelligence called deep learning. The DeepDream project is now available on GitHub. The project relies on the open-source Caffe deep learning framework. Deep learning involves training artificial neural networks on a large pile of data — for example, pictures of geese — and then throwing them a new piece of data, like a picture of an ostrich, to receive an educated guess about it.

Learning from the best Guest contributor David Kofoed Wind is a PhD student in Cognitive Systems at The Technical University of Denmark (DTU): As a part of my master's thesis on competitive machine learning, I talked to a series of Kaggle Masters to try to understand how they were consistently performing well in competitions. What I learned was a mixture of rather well-known tactics, and less obvious tricks-of-the-trade. In this blog post, I have picked some of their answers to my questions in an attempt to outline some of the strategies which are useful for performing well on Kaggle. As the name of this blog suggests, there is no free hunch, and reading this blog post will not make you a Kaggle Master overnight.

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