Practical Design Of A Neural Network In C++[Step By Step]

Neural network Simulator design in C++

What you'll learn

The student will learn how to design the structure of a neural network including its neurons, bias, input layer, hidden layers, output layers and weights

The student will have a clear understanding of how the feed forward mechanism is used to shift inputs from the input layer through the output layer

The student will learn how to calculate the Root Mean Square error, output and hidden gradient, transfer function and the derivatives for a neural network

The student will also carryout back propagation on a neural network after the feed forward and use it for adjusting the weights of the neurons.

The student will learn how to generate training samples

Requirements

Theoritical Understanding of neural network, feed forward and back propagation

Basic knowledge of C++ is required

Description

This course teaches the practical design of a Neural network simulator using C++. It is recommended for all levels of C++ programmers with a theoretical knowledge of Neural network and looking forward to implement them in practice. The course interactively simulates the Neural network from the design of the class called Neuron, to the implementation of the Neuron layers in Vectors and finally the top level design consisting of the input layer, hidden layer and the output layer. Some random training samples will be generated which will be feed to the input layer through a vector and progress to the output layer through feed forward. The back propagation is also implemented which enables us to calculate the error and update the weight for a more accurate result. The training samples used in this course is for demonstration as the concept of sample generation is well explained. At the end of the course the student should be able generate real samples for testings. Some of the Core concepts we will learn in this course includes:Feed forward .Bias Neuron.Transfer function.Back propagation.Activation function.Root mean square error.Transfer function derivative.Generating training samples.Output and hidden layer Gradient.Some of the C++ concepts used includes:Assert()prototypingClass designNested VectorsReference VariablesStatic class variablesData hiding and encapsulation

Overview

Section 1: Introduction

Lecture 1 01. Introduction

Lecture 2 downloadable zip , course files

Lecture 3 02. Accessing the Number of layers and Neurons from command line

Lecture 4 03. Generating the Training samples

Lecture 5 04. Developing the Neural Network structure

Lecture 6 05. Testing our Neural Network structure

Lecture 7 06. Extracting our Training samples from text file

Lecture 8 07. Implementing the feed forward mechanism

Lecture 9 08. Extracting and displaying the results of our training

Lecture 10 09. Back propagation and calculating RMS and gradients

Lecture 11 10. Updating the weight of our Neurons

Lecture 12 11. Exercise and conclusion

All levels C++ developer