At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learning?; Challenges with Machine Learning; Overfitting; Confronting Overfitting; Types of Machine Learning; Classification and Regression; Summary; Chapter 2: Neural Network; Nodes of a Neural Network; Layers of Neural Network; Supervised Learning of a Neural Network; Training of a Single-Layer Neural Network: Delta Rule; Generalized Delta Rule; SGD, Batch, and Mini Batch; Stochastic Gradient Descent; Batch; Mini Batch

Example: Delta RuleImplementation of the SGD Method; Implementation of the Batch Method; Comparison of the SGD and the Batch; Limitations of Single-Layer Neural Networks; Summary; Chapter 3: Training of Multi-Layer Neural Network; Back-Propagation Algorithm; Example: Back-Propagation; XOR Problem; Momentum; Cost Function and Learning Rule; Example: Cross Entropy Function; Cross Entropy Function; Comparison of Cost Functions; Summary; Chapter 4: Neural Network and Classification; Binary Classification; Multiclass Classification; Example: Multiclass Classification; Summary

Chapter 5: Deep LearningImprovement of the Deep Neural Network; Vanishing Gradient; Overfitting; Computational Load; Example: ReLU and Dropout; ReLU Function; Dropout; Summary; Chapter 6: Convolutional Neural Network; Architecture of ConvNet; Convolution Layer; Pooling Layer; Example: MNIST; Summary; Index