1. Guide to Networking Essentials, Pre-Owned (Paperback)

Pre-Owned - GUIDE TO NETWORKING ESSENTIALS provides students with both the knowledge and hands-on skills necessary to work with network operating systems in a network administration environment. By focusing on troubleshooting and computer networking technologies, this book offers a comprehensive introduction to networking and to advances in software, wireless and network security. Challenge Labs and Hands-On Projects are directly integrated in each chapter to allow for a hands-on experience in the classroom. Updated content reflects the latest networking technologies and operating systems including new Ethernet standards, cloud computing, Windows 10, Windows Server 2016, and recent Linux distributions.

Guide to Networking Essentials, Pre-Owned (Paperback)

2. Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, Pre-Owned (Paperback)

Pre-Owned - Graphics in this book are printed in black and white.Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details

Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, Pre-Owned (Paperback)

3. Theoretical Mechanics of Biological Neural Networks, Used [Hardcover]

Theoretical Mechanics of Biological Neural Networks presents an extensive and coherent discusson and formulation of the generation and integration of neuroelectric signals in single neurons. The approach relates computer simulation programs for neurons of arbitrary complexity to fundamental gating processes of transmembrance ionic fluxes of synapses of excitable membranes. Listings of representative computer programs simulating arbitrary neurons, and local and composite neural networks are included.

Theoretical Mechanics of Biological Neural Networks, Used [Hardcover]

4. Optimality in Biological and Artificial Networks?, Used [Hardcover]

This book is the third in a series based on conferences sponsored by the Metroplex Institute for Neural Dynamics, an interdisciplinary organization of neural network professionals in academia and industry. The topics selected are of broad interest to both those interested in designing machines to perform intelligent functions and those interested in studying how these functions are actually performed by living organisms and generate discussion of basic and controversial issues in the study of mind. The topic of optimality was chosen because it has provoked considerable discussion and controversy in many different academic fields. There are several aspects to the issue of optimality. First, is it true that actual behavior and cognitive functions of living animals, including humans, can be considered as optimal in some sense? Second, what is the utility function for biological organisms, if any, and can it be described mathematically? Rather than organize the chapters on a "biological versus artificial" basis or by what stance they took on optimality, it seemed more natural to organize them either by what level of questions they posed or by what intelligent functions they dealt with. The book begins with some general frameworks for discussing optimality, or the lack of it, in biological or artificial systems. The next set of chapters deals with some general mathematical and computational theories that help to clarify what the notion of optimality might entail in specific classes of networks. The final section deals with optimality in the context of many different high-level issues, including exploring one's environment, understanding mental illness, linguistic communication, and social organization. The diversity of topics covered in this book is designed to stimulate interdisciplinary thinking and speculation about deep problems in intelligent system organization.

Optimality in Biological and Artificial Networks?, Used [Hardcover]

5. How Smart Machines Think, Used [Paperback]

Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world--and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy , and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution--at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

How Smart Machines Think, Used [Paperback]

6. Learning and Categorization in Modular Neural Networks, Used [Paperback]

This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume. The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.

Learning and Categorization in Modular Neural Networks, Used [Paperback]

7. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence [Hardcover - Used]

CONDITION - USED - Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. Accessories such as CD, codes, toys, may not be included. This Third Edition provides the latest tools and techniques that enable computers to learn The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.

Evolutionary Computation : Toward a New Philosophy of Machine Intelligence, Used [Hardcover]

8. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence/Book and Disk [Hardcover - Used]

CONDITION - USED - Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. Accessories such as CD, codes, toys, may not be included. Combining neural networks and fuzzy systems, this presents neural networks as trainable dynamical systems and develops mechanisms and principles of adaption, self-organization, covergence and global stability. It also includes the geometric theory of fuzzy sets, systems and associative memories.

Neural Networks and Fuzzy Systems : A Dynamical Systems Approach to Machine Intelligence, Used [Hardcover]

9. Fuzzy-Neural Systems for Image Understanding, Used [Hardcover]

Fuzzy logic is an approach to computing that relates to the function of the brain. This title applies the principles of fuzzy-neural systems to areas of image understanding and computer vision such as remote sensing, medical image analysis, robot vision, and military operatives.

Fuzzy-Neural Systems for Image Understanding, Used [Hardcover]

10. Neural Networks for Control (Neural Network Modeling and Connectionism) [Hardcover - Used]

CONDITION - USED - Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions. Accessories such as CD, codes, toys, may not be included. Neural Networks for Control highlights key issues in learning control and identifies research directions that could lead to practical solutions for control problems in critical application domains. It addresses general issues of neural network based control and neural network learning with regard to specific problems of motion planning and control in robotics, and takes up application domains well suited to the capabilities of neural network controllers. The appendix describes seven benchmark control problems. ContributorsAndrew G. Barto, Ronald J. Williams, Paul J. Werbos, Kumpati S. Narendra, L. Gordon Kraft, III, David P. Campagna, Mitsuo Kawato, Bartlett W. Met, Christopher G. Atkeson, David J. Reinkensmeyer, Derrick Nguyen, Bernard Widrow, James C. Houk, Satinder P. Singh, Charles Fisher, Judy A. Franklin, Oliver G. Selfridge, Arthur C. Sanderson, Lyle H. Ungar, Charles C. Jorgensen, C. Schley, Martin Herman, James S. Albus, Tsai-Hong Hong, Charles W. Anderson, W. Thomas Miller, III

Neural Networks for Control, Used [Hardcover]