ARTIFICIAL INTELLIGENCE

 MACHINE LEARNING 

Some useful resources for students of this field.

A computer is said to learn:

from experience E 

with respect to task T 

with some performance measurement P

 if its performance on T, as measured by P, improves with experience E.

-Tom Mitchell -

Here’s a well demonstrated overview of a neural network. Programming this example is a delightful exercise and a “must do” for any student of neural networks.

For another approach to designing a network model for continuous processes, see the Articles tab on this site for the article "Neural Ordinary Differential Equations". The authors introduce a family of deep neural network models (ODE nets) which do not use a discrete sequence of hidden neural net layers with nodes and connections.

 An overview of a Convolutional Neural Network (CNN/ConvNet)

 Gradient Descent optimization demonstration - a bit easier than conceptualizing with contour plots.

 A lovely demonstration of back-propagation – much better than computing it by hand to understand the finer points: