Neural Network Design (2nd Edition)

This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.

• Make Your Own Neural Network
• Deep Learning (Adaptive Computation and Machine Learning series)
• Fundamentals of Artificial Neural Networks (MIT Press)
• Deep Learning: A Practitioner's Approach
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
• Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
• Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
• Python Machine Learning, 1st Edition
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)