Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks

Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.

Table of Contents

Chapter 1: Neural Network Basics
Chapter 2: Self-Organizing Maps
Chapter 3: Hopfield & Boltzmann Machines
Chapter 4: Feedforward Neural Networks
Chapter 5: Training & Evaluation
Chapter 6: Backpropagation Training
Chapter 7: Other Propagation Training
Chapter 8: NEAT, CPPN & HyperNEAT
Chapter 9: Deep Learning
Chapter 10: Convolutional Neural Networks
Chapter 11: Pruning and Model Selection
Chapter 12: Dropout and Regularization
Chapter 13: Time Series and Recurrent Networks
Chapter 14: Architecting Neural Networks
Chapter 15: Visualization
Chapter 16: Modeling with Neural Networks
Appendix A: Examples