Discrete Neural Computation
Download Discrete Neural Computation PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Discrete Neural Computation book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Discrete Neural Computation
Written by the three leading authorities in the field, this book brings together -- in one volume -- the recent developments in discrete neural computation, with a focus on neural networks with discrete inputs and outputs. It integrates a variety of important ideas and analytical techniques, and establishes a theoretical foundation for discrete neural computation. Discusses the basic models for discrete neural computation and the fundamental concepts in computational complexity; establishes efficient designs of threshold circuits for computing various functions; develops techniques for analyzing the computational power of neural models. A reference/text for computer scientists and researchers involved with neural computation and related disciplines.
Discrete Neural Computation Basics
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-04-26
Discrete neural computation is a rapidly growing field that has the potential to revolutionize many aspects of our lives. Discrete neural networks are a type of artificial neural network that uses discrete-valued inputs and outputs instead of continuous-valued inputs and outputs. This makes them particularly well-suited for applications in areas such as computer vision, natural language processing, and robotics. In this book, we provide a comprehensive introduction to the foundations of discrete neural computation. We cover everything from the basic principles of discrete neural network learning to the latest advances in discrete neural network optimization. We also explore a variety of applications of discrete neural networks, including image classification, object detection, speech recognition, and robot control. The book is divided into five chapters. The first chapter provides an introduction to discrete neural computation. We discuss the history of discrete neural computation, the different types of discrete neural networks, and the applications of discrete neural networks. The second chapter covers the basic principles of discrete neural network learning. We discuss the different types of learning algorithms that are used to train discrete neural networks, and we explore the challenges that are associated with training discrete neural networks. The third chapter covers the different types of optimization techniques that are used to improve the performance of discrete neural networks. We discuss the different types of optimization algorithms that are used to train discrete neural networks, and we explore the challenges that are associated with optimizing discrete neural networks. The fourth chapter covers a variety of applications of discrete neural networks. We discuss the use of discrete neural networks for image classification, object detection, speech recognition, and robot control. We also explore the challenges that are associated with using discrete neural networks for these applications. The fifth chapter provides a summary of the key points that were covered in the book. We also discuss the future directions of research in discrete neural computation. This book is intended for readers with a basic understanding of artificial neural networks. It is also intended for readers who are interested in learning more about the foundations of discrete neural computation. We hope that this book will provide readers with a comprehensive understanding of the foundations of discrete neural computation. We also hope that this book will inspire readers to explore the many potential applications of discrete neural networks. If you like this book, write a review on google books!
Handbook of Neural Computation
The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl