Tags » Neural Networks

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

Healthinnovations

A Gentle Introduction to Artificial Neural Networks

Introduction

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

Regression

Derivation: Derivatives for Common Neural Network Activation Functions

Introduction

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

Regression

[Machine Learning] A first look at WEKA

Just played with WEKA this morning. It can be downloaded from http://www.cs.waikato.ac.nz/ml/weka/downloading.html and there’s quite a lot of documentation online. As usual, just my few cents worth of screenshots. 28 more words

Derivation: Error Backpropagation & Gradient Descent for Neural Networks

Introduction

Artificial neural networks (ANNs) are a powerful class of models used for nonlinear regression and classification tasks that are motivated by biological neural computation. The general idea behind ANNs is pretty straightforward: map some input onto a desired target value using a distributed cascade of nonlinear transformations (see Figure 1). 1,578 more words

Regression

SummaReview: Plant Intelligence and The Imaginal Realm

“…you must not extend awareness further than your culture wants it to go.” (19)

This is Buhner’s fourth book in his series of books on plant intelligence, inter-species communication, non-linearity in nature, and the various non-linear and intuitive capacities in humans. 609 more words

Food For Thought

A demo of neural networks

I enjoyed this demo of how exactly neural nets work:

A visual proof that neural nets can compute any function

I liked the interactive demo. I feel like I have a better intuitive understanding of weight and bias mean.

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Self-Teaching