Evolutionary Deep Neural Network Design

Author : Yanan Sun
Publisher : Wiley-IEEE Press
ISBN 13 : 9781119699859
Total Pages : 230 pages
Book Rating : 4.51/5 ( download)

DOWNLOAD NOW!

Download or read book Evolutionary Deep Neural Network Design written by Yanan Sun and published by Wiley-IEEE Press. This book was released on 2020-12-30 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the details of concepts, the methods and the challenges of evolutionary deep neural networks design. The authors begin by providing a brief introduction to deep neural networks, evolutionary computation. They also include some representative examples of both. Then they move on to describing the scope of evolutionary deep neural network design, and the fundamental methods of evolutionary deep neural network architecture design. Finally, they highlight the main challenges and some potential research directions in this emerging topic.

Author : Hitoshi Iba
Publisher : Springer Nature
ISBN 13 : 9811536856
Total Pages : 437 pages
Book Rating : 4.54/5 ( download)

DOWNLOAD NOW!

Download or read book Deep Neural Evolution written by Hitoshi Iba and published by Springer Nature. This book was released on 2020-05-20 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.

Author : Seyedali Mirjalili
Publisher : Springer
ISBN 13 : 3319930257
Total Pages : 156 pages
Book Rating : 4.51/5 ( download)

DOWNLOAD NOW!

Download or read book Evolutionary Algorithms and Neural Networks written by Seyedali Mirjalili and published by Springer. This book was released on 2018-06-26 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.

Author : Yanan Sun
Publisher : Springer Nature
ISBN 13 : 3031168682
Total Pages : 335 pages
Book Rating : 4.80/5 ( download)

DOWNLOAD NOW!

Download or read book Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances written by Yanan Sun and published by Springer Nature. This book was released on 2022-11-08 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.

Author : Michael Lanham
Publisher : Simon and Schuster
ISBN 13 : 1617299529
Total Pages : 358 pages
Book Rating : 4.20/5 ( download)

DOWNLOAD NOW!

Download or read book Evolutionary Deep Learning written by Michael Lanham and published by Simon and Schuster. This book was released on 2023-07-18 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser- known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time you’ve finished reading Evolutionary Deep Learning, you’ll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Author : Rudolf F. Albrecht
Publisher : Springer Science & Business Media
ISBN 13 : 370917533X
Total Pages : 752 pages
Book Rating : 4.30/5 ( download)

DOWNLOAD NOW!

Download or read book Artificial Neural Nets and Genetic Algorithms written by Rudolf F. Albrecht and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.

Author : Martin T. Hagan
Publisher :
ISBN 13 : 9789812403766
Total Pages : pages
Book Rating : 4.60/5 ( download)

DOWNLOAD NOW!

Download or read book Neural Network Design written by Martin T. Hagan and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : R P Johnson
Publisher : World Scientific
ISBN 13 : 9814497495
Total Pages : 194 pages
Book Rating : 4.97/5 ( download)

DOWNLOAD NOW!

Download or read book Automatic Generation Of Neural Network Architecture Using Evolutionary Computation written by R P Johnson and published by World Scientific. This book was released on 1997-10-31 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.85/5 ( download)

DOWNLOAD NOW!

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Author : Daniel Graupe
Publisher : World Scientific Publishing Company
ISBN 13 : 9813146478
Total Pages : 280 pages
Book Rating : 4.71/5 ( download)

DOWNLOAD NOW!

Download or read book Deep Learning Neural Networks written by Daniel Graupe and published by World Scientific Publishing Company. This book was released on 2016-07-07 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance. This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.