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The Backpropagation Network

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This program is copyright © 1996 by the author. It is made available as is, and no warranty - about the program, its performance, or its conformity to any specification - is given or implied. It may be used, modified, and distributed freely for private and commercial purposes, as long as the original author is credited as part of the final work.

Understanding Neural Network Batch Training: A Tutorial

Understanding Neural Network Batch Training: A Tutorial

There are two different techniques for training a neural network: batch and online. Understanding their similarities and differences is important in order to be able to create accurate prediction systems.

namespace DropoutDemo

using System;
using System.Collections.Generic;

namespace DropoutDemo
{
  class DropoutProgram

BackPropTraining

using System;
namespace BackPropTraining
{
  class BackPropTrainingProgram

BackPropTraining

using System;
namespace BackPropTraining
{
  class BackPropTrainingProgram
  {
    static void Main(string[] args)
    {
      Console.WriteLine("\nBegin neural network training with back-propagation demo\n");
      Console.WriteLine("\nData is the famous Iris flower set.");
      Console.WriteLine("Data is sepal length, sepal width, petal length, petal width -> iris species");
      Console.WriteLine("Iris setosa = 0 0 1, Iris versicolor = 0 1 0, Iris virginica = 1 0 0 ");
      Console.WriteLine("The goal is to predict species from sepal length, sepal width, petal length, petal width\n");
  

Neural Network Back-Propagation Using C#

using System;
// demonstration of neural network back-propagation
namespace BackProp
{
  class BackPropProgram
  {
    static void Main(string[] args)
    {
      try

demonstration of neural network back-propagation

using System;
// demonstration of neural network back-propagation