Multilayer Neural Network Implementation
 
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. The course concludes with a summary of neural network training.

**Key Learning Points:**

- Understand the fundamental theory of multi-layer neural networks.
- Grasp the mathematics behind neural network training, focusing on Backpropagation and Gradient Descent.
- Explore the principles of neuron activation functions mathematically.
- Learn step-by-step procedures for training and testing neural networks.
- Acquire practical skills in implementing multi-layer neural network training and testing using C#.
- Develop proficiency in creating tailored datasets for neural network training and testing.

**What you'll learn:**

- Basic theory of Multi-Layer Neural Networks
- Mathematics of Neural Network Training: Backpropagation and Gradient Descent
- Mathematics of Neuron Activation Functions
- Implementation of Training and Testing a Multi-Layer Neural Network in C#
- Creation of Datasets for Training and Testing Neural Networks

**Requirements:**

- Basic understanding of Linear Algebra
- Intermediate proficiency in C#
- Basic knowledge of JSON