You can look at the sample code and figure out for yourself what is what. The idea for this post is to give you an idea on what Neural Networks are and how they work. It would be too big of a job to go through each functionality bit by bit. Then this post will move onto theory where hopefully you will find a better understanding of how Neural Networks work.Īlso I will not be showing off most of the code in these example because there is literally thousands of lines of code. I’ll start off with the sample projects to give you an idea what can be done. They are written in C# and running on Unity. ![]() The code examples here will be based on the sample projects found in the book AI Game Programming Techniques by (Buckland, 2002 ). ![]() I did not feel like trying to minimize the explanations in the sources I was reading and learning since they seemed already strict and straightforward. These are my bits of information which helped me understand Neural Networks. Mostly this is just my notes on different sources of information regarding Neural Networks. This is going to be another long blog post. Minimizing Dimensionality through Complexification. Protecting Innovation through Speciation. How Innovations Help in the Design of a Valid Crossover Operator. Tracking Genes through Historical Markings. Promising solution: NEAT – Neuro Evolution of Augmenting Topologies. 39ĮANN – Evolutionary Artificial Neural Network. ![]() Requirements and problems of neural networks. 10Ĭlassification of NN systems with respect to learning methods and architecture types. TAXONOMY OF NEURAL NETWORK ARCHITECTURES. Sweepers – NEAT – Neuro Evolution of Augmenting Topologies Neural Network example. Minesweepers – Unsupervised learning Neural Network example.
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