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All Blog Posts Tagged 'networks' (27)

Theano Tensors Explained in a Picture

Lately I've been doing some experiences with Theano and Deep Learning. One thing that I really thought could help is to understand the workflow of a Theano algorithm through visualization of tensors' connections. After developing the model, I printed the prediction algorithm for a deep learning Neural Net with 2 hidden layers, 2 inputs X1 and X2, and a continuous output Y. I used Graphviz and pydot to generate the graphic with this line of…

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Added by Rubens Zimbres on October 7, 2016 at 3:00am — 3 Comments

A primer on universal function approximation with deep learning (in Torch and R)

Arthur C. Clarke famously stated that “any sufficiently advanced technology is indistinguishable from magic.” No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people’s hearts. This primer sheds some light on how neural networks work, hopefully adding to the wonder while reducing the fear.

One known property of artificial neural networks (ANNs) is that they are…

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Added by Brian Rowe on September 27, 2016 at 1:30pm — No Comments

The Emerging World of Neural Net Driven MT

Originally posted here, where you can see all the graphics

There has been much in the news lately about the next wave of MT technology driven by a technology called deep learning and neural nets (DNN). I will attempt to provide a brief layman’s overview about what this is, even though I am barely qualified to do this (but if Trump can run for POTUS then…

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Added by Kirti Vashee on July 29, 2016 at 9:30am — No Comments

Machine Learning - Some Bones







Machine learning is a loose term, it covers many activities. From a software engineering…

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Added by Bruce Robbins on June 30, 2016 at 12:18am — No Comments

Step-by-step video courses for Deep Learning and Machine Learning

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! …

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Added by LazyProgrammer.me on January 23, 2016 at 8:30pm — 2 Comments

Training Neural Networks: Q&A with Ian Goodfellow, Google

Neural networks require considerable time and computational firepower to train. Previously, researchers believed that neural networks were costly to train because gradient descent slows down near local minima or saddle points. At the RE.WORK Deep…

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Added by Sophie Curtis on September 3, 2015 at 8:59am — No Comments

High Performance Computing + Data Science = Competitive Advantage

High Performance Computing (HPC) plus data science allows public and private organizations get…

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Added by Michael Walker on September 17, 2013 at 12:28pm — 1 Comment

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