Tags » Neural Networks

Can Companies Learn Your Secrets

Description:  “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that? ” 205 more words

Business Model

Derivation: Maximum Likelihood for Boltzmann Machines

In this post I will review the gradient descent algorithm that is commonly used to train the general class of models known as Boltzmann machines. Though the primary goal of the post is to supplement another post on restricted Boltzmann machines, I hope that those readers who are curious about how Boltzmann machines are trained, but have found it difficult to track down a complete or straight-forward derivation of the maximum likelihood learning algorithm for these models (as I have), will also find the post informative. 815 more words


How To Build and Use a Multi GPU System for Deep Learning

When I started using GPUs for deep learning my deep learning skills improved quickly. When you can run experiments of algorithms and algorithms with different parameters and gain rapid feedback you can just learn much more quickly. 948 more words

Deep Learning

Making Sense of IoT Data With Machine Learning Technologies

Source: http://www.forbes.com/sites/mikekavis/2014/09/04/making-sense-of-iot-data-with-machine-learning-technologies/

In a previous post I discussed how the Internet of Things (IoT) will radically change your big data strategy. Massive amounts of data from sensors, wearable devices, and other technologies are creating new and exciting opportunities to make better business decisions in real time. 680 more words


Scientists begin to map neurodevelopment of schizophrenia.

Schizophrenia is generally considered to be a disorder of brain development and it shares many risk factors, both genetic and environmental, with other neurodevelopmental disorders such as autism and intellectual disability.  501 more words


A Gentle Introduction to Artificial Neural Networks


Though many phenomena in the world can be adequately modeled using linear regression or classification, most interesting phenomena are generally nonlinear in nature. In order to deal with nonlinear phenomena, there have been a diversity of nonlinear models developed. 5,824 more words


Derivation: Derivatives for Common Neural Network Activation Functions


When constructing Artificial Neural Network (ANN) models, one of the primary considerations is choosing activation functions for hidden and output layers that are differentiable. This is because calculating the backpropagated error signal that is used to determine ANN parameter updates requires the gradient of the activation function gradient . 738 more words