Neural Networks Using C# Succinctly

Syncfusion Inc., 2014. — 128 p. With Neural Networks Using C# Succinctly by James McCaffrey, you’ll learn how to create your own neural network to solve classification problems, or problems where the outcomes can only be one of several values. Learn how to encode and normalize qualitative data into numeric data a neural network can use, different activation functions and when to use them, and ultimately how to train a neural network to find weights and bias values that provide accurate predictions. Topics included: Neural Networks Perceptrons Feed-Forward Back-Propagation Training. The Story behind the Succinctly Series of Books 7 About the Author 9 Acknowledgements 10 Chapter 1 Neural Networks 11 Introduction 11 Data Encoding and Normalization 13 Overall Demo Program Structure 14 Effects Encoding and Dummy Encoding 18 Min-Max Normalization 23 Gaussian Normalization 24 Complete Demo Program Source Code 25 Chapter 2 Perceptrons 30 Introduction 30 Overall Demo Program Structure 32 The Input-Process-Output Mechanism 32 The Perceptron Class Definition 34 The ComputeOutput Method 35 Training the Perceptron 36 Using the Perceptron Class 40 Making Predictions 42 Limitations of Perceptrons 43 Complete Demo Program Source Code 44 Chapter 3 Feed-Forward 49 Introduction 49 Understanding Feed-Forward 50 Bias Values as Special Weights 52 Overall Demo Program Structure 52 Designing the Neural Network Class 54 The Neural Network Constructor 56 Setting Neural Network Weights and Bias Values 57 Computing Outputs 58 Activation Functions 62 Complete Demo Program Source Code 66 Chapter 4 Back-Propagation 70 Introduction 70 The Basic Algorithm 71 Computing Gradients 72 Computing Weight and Bias Deltas 73 Implementing the Back-Propagation Demo 75 The Neural Network Class Definition 78 The Neural Network Constructor 79 Getting and Setting Weights and Biases 81 Computing Output Values 82 Implementing the FindWeights Method 84 Implementing the Back-Propagation Algorithm 85 Complete Demo Program Source Code 88 Chapter 5 Training 95 Introduction 95 Incremental Training 96 Implementing the Training Demo Program 97 Creating Training and Test Data 99 The Main Program Logic 102 Training and Error 105 Computing Accuracy 109 Cross Entropy Error 112 Binary Classification Problems 114 Complete Demo Program Source Code 116 neural networks,C#,machine learning,Syncfusion,Succinctly series Introduction to neural networks.