Learn Neural Net Programming: http://www.heatonresearch.com/course/intro-neural-nets-cs
In class session 3, part 2 we will look at how to actually program a Hopfield neural network in C#. The hopfield neural network is a simple neural network made up of bipolar numbers. Artificial intelligence online course presented by Jeff Heaton, Heaton Research.
Duration : 0:9:18
This video reviews the basic architecture of the Encog ( http://www.encog.org ) Artificial Intelligence Framework. The major object types of layers, synapses, and neural logic are reviewed. Feedforward, Elman and Hopfield neural networks are demonstrated.
Duration : 0:9:30
Learn Neural Net Programming: http://www.heatonresearch.com/course/intro-neural-nets-java
In class session 3, part 1 we will look at the structure of the Hopfield neural network. The hopfield neural network is a simple neural network made up of bipolar numbers. Artificial intelligence online course presented by Jeff Heaton, Heaton Research.
Duration : 0:9:58
Learn Neural Net Programming: http://www.heatonresearch.com/course/intro-neural-nets-java
Introduction to Neural Networks with java is a course that introduces the java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures will be presented. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples will be examined that include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.
Duration : 0:9:35
Learn Neural Net Programming: www.heatonresearch.com Introduction to Neural Networks with C# is a course that introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures will be presented. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples …
Duration : 0:10:1