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All Blog Posts Tagged 'neural' (38)

Generative Adversarial Networks (GANs): Engine and Applications


Generative adversarial networks (GANs) are a class of neural networks that are used in unsupervised machine learning. They help to solve such tasks as image generation from descriptions, getting high resolution images from low resolution ones, predicting which drug…

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Added by Luba Belokon on August 17, 2017 at 6:30am — No Comments

Building Convolutional Neural Networks with Tensorflow

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications.

The contents of this blog-post is as follows:

  1. Tensorflow basics:
    • 1.1 Constants and Variables
    • 1.2 Tensorflow Graphs and Sessions
    • 1.3 Placeholders and…
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Added by Ahmet Taspinar on August 15, 2017 at 4:00am — No Comments

Recasting Java neural networks in Python

Many neural network applications implemented in Java, such as Neuroph, Encog and Joone, may look rather different when switching from the Java language to Python with the help of the DMelt computing environment. First of all, they look simpler. You can use your favorite Python tricks to load and display data. The Python coding is simpler for viewing and fast modifications. It does not require recompiling after each change. At the same time, the platform…

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Added by jwork.ORG on July 29, 2017 at 1:00pm — No Comments

Quantum Computing, Deep Learning, and Artificial Intelligence

Summary:  Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models.  Here are a few things you need to know.

 

So far in this series of articles on Quantum computing we showed that…

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Added by William Vorhies on June 13, 2017 at 8:00am — No Comments

Retropropagation with SAS

A code of mine that works well with different data types.

I like comments by programmers who like me are writing a feed forward neural network

with two hidden layers (arch: X-S-H-Y) using SAS.

%macro CalculateLayer;

/* Calculate S Layer */

%do i=1 %to &SLayer.; /* S Layer */…

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Added by Francesco D'Annibale on June 6, 2017 at 4:00am — No Comments

Deciphering the Neural Language Model

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 has to do lots of extra work in order to understand the topics covered in full detail.

One of the assignments in…

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Added by Burak Himmetoglu on December 17, 2016 at 10:00am — No Comments

Neural Networks: The Backpropagation algorithm in a picture

Here I present the backpropagation algorithm for a continuous target variable and no activation function in hidden layer: although simpler than the one used for the logistic cost function, it's a proficuous field for math lovers.

Added by Rubens Zimbres on November 19, 2016 at 9:30am — 3 Comments

Matrix Multiplication in Neural Networks

This post is the outcome of my studies in Neural Networks and a sketch for application of the Backpropagation algorithm. It's a binary classification task with N = 4 cases in a Neural Network with a single hidden layer. After the hidden layer and the output layer there are sigmoid activation functions. Different colors were used in the Matrices, same color as the Neural Network structure (bias, input, hidden, output) to make it easier to understand.…

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Added by Rubens Zimbres on November 16, 2016 at 11:00am — 4 Comments

24 Neural Network Adjustements

Added by Rubens Zimbres on October 27, 2016 at 7:30am — No Comments

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

On-going Developments and Outlook for Deep Learning

Overview

There are huge numbers of variants of deep architectures as it’s a fast developing field and so it helps to mention other leading algorithms. The list is intended to be comprehensive but not exhaustive since so many algorithms are being developed [1] [2][1],[2].

  1. Deep High-order Neural Network with Structured Output (HNNSO).
  2. Deep convex network.
  3. Spectral networks
  4. noBackTrack algorithm to solve the…
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Added by Syed Danish Ali on July 26, 2016 at 5:00am — 2 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

Structural Relationships in Data

The first computer program that I encountered mimicking or emulating human interaction through language was called "Eliza." The version that I knew ran on the Commodore PET. It communicated in English. Eliza made comments that made some sense but which indicated lack of understanding of the conversation. If a person mentions "mother," Eliza might…

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Added by Don Philip Faithful on June 20, 2015 at 5:06am — 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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