What you'll learn
Basic theory of Multi-Layer Neural Networks
The mathematics of Neural Network Training: Backpropagation and Gradient Descent
The mathematics of Neuron Activation Functions
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
Requirements
Basic understanding of Linear Algebra
Intermediate proficiency in C#
Basic knowledge of JSON
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 CourseBasic theory of Multi-Layer Neural NetworksThe mathematics of Neural Network Training: Backpropagation and Gradient DescentThe mathematics of Neuron Activation FunctionsThe process for training and testing the Neural NetworkHow to implement in C# the Training and Testing of a Multi-Layer Neural NetworkHow to create Datasets for Training and Testing the Neural NetworkCourse OutlineSection 1: IntroductionCourse OverviewIntroduction to Neural NetworksMulti-Layer Neural Network StructureSection 2: Mathematics of Neural Network TrainingMulti-Layer Neural Network TrainingSection 3: ImplementationTraining ProcessTesting ProcessAnalysis of the Source CodeSection 4: DatasetsTesting with DatasetsSection 5: SummaryQuick ReviewSection 6: ExerciseExercise
Overview
Section 1: Introduction
Lecture 1 Course Overview
Lecture 2 Introduction to Neural Networks
Lecture 3 Multi-Layer Neural Network Structure
Section 2: Mathematics of Neural Network Training
Lecture 4 Multi-Layer Neural Network Training
Section 3: Implementation
Lecture 5 Training Process
Lecture 6 Testing Process
Lecture 7 Analysis of the Source Code
Section 4: Datasets
Lecture 8 Testing with Datasets
Section 5: Summary
Lecture 9 Quick Review
Section 6: Exercise
Lecture 10 Exercise
IT Professionals and Software Engineers who want to understand the mathematics and implementation of Multi-Layer Neural Networks
Homepage

https://www.udemy.com/course/multi-layer-neural-network-implementation/