Multi-Layer Neural Network Implementation

Description

This course presents in detail the implementation of multi-layer neural network training and testing. The steps involved in neural network training and testing are discussed in detail with thorough review of the mathematics. The C# source code, that is available for download, is discussed in detail.  Testing with datasets is presented with the aim of being applicable to any prediction problem use case.  The course begins with a thorough introduction to neural networks, provides a detailed view of the structure of multi-layer neural networks, presents the mathematics involved in neural network training in a very simple and methodical approach, presents the demonstration of testing with a number of datasets, and ends with a quick summary of neural network training.

What you will learn in the Course

• Basic theory of Multi-Layer Neural Networks

• The mathematics of Neural Network Training: Backpropagation and Gradient Descent

• The mathematics of Neuron Activation Functions

• The process for training and testing the Neural Network

• How to implement in C# the Training and Testing of a Multi-Layer Neural Network

• How to create Datasets for Training and Testing the Neural Network

Course Outline

Section 1: Introduction

• Course Overview

• Introduction to Neural Networks

• Multi-Layer Neural Network Structure

Section 2: Mathematics of Neural Network Training

• Multi-Layer Neural Network Training

Section 3: Implementation

• Training Process

• Testing Process

• Analysis of the Source Code

Section 4: Datasets

• Testing with Datasets

Section 5: Summary

• Quick Review

Section 6: Exercise

• Exercise

Who this course is for:

• IT Professionals and Software Engineers who want to understand the mathematics and implementation of Multi-Layer Neural Networks