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

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Description

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.

A special thank you to the repeat backers of this book series

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 and 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: Modelling with Neural Networks