This course comprehensively explores training and testing for multi-layer neural networks, covering detailed steps and underlying mathematics. The accompanying C# source code is thoroughly explained, emphasizing practical testing with datasets applicable to diverse prediction scenarios.Beginning with an extensive introduction to neural networks, the curriculum proceeds to delve into the structure of multi-layer neural networks. Mathematical aspects of training, including Backpropagation and Gradient Descent, are presented systematically.

Additionally, the course elucidates the mathematics behind neuron activation functions.Throughout the learning journey, diverse datasets are utilized for testing, ensuring relevance to various prediction problems.