05291nam 22005415 450 99646546910331620200704235005.0981-15-3685-610.1007/978-981-15-3685-4(CKB)4100000011254370(MiAaPQ)EBC6207631(DE-He213)978-981-15-3685-4(PPN)248392794(EXLCZ)99410000001125437020200520d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Neural Evolution[electronic resource] Deep Learning with Evolutionary Computation /edited by Hitoshi Iba, Nasimul Noman1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (437 pages)Natural Computing Series,1619-7127981-15-3684-8 Includes bibliographical references and index.Chapter 1: Evolutionary Computation and meta-heuristics -- Chapter 2: A Shallow Introduction to Deep Neural Networks -- Chapter 3: On the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks -- Chapter 4: Automated development of DNN based spoken language systems using evolutionary algorithms -- Chapter 5: Search heuristics for the optimization of DBN for Time Series Forecasting -- Chapter 6: Particle Swarm Optimisation for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-objective Approaches -- Chapter 7: Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming -- Chapter 8: Fast Evolution of CNN Architecture for Image Classificaiton -- Chapter 9: Discovering Gated Recurrent Neural Network Architectures -- Chapter 10: Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution -- Chapter 11: Neuroevolution of Generative Adversarial Networks -- Chapter 12: Evolving deep neural networks for X-ray based detection of dangerous objects -- Chapter 13: Evolving the architecture and hyperparameters of DNNs for malware detection -- Chapter 14: Data Dieting in GAN Training -- Chapter 15: One-Pixel Attack: Understanding and Improving Deep Neural Networks with Evolutionary Computation.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.Natural Computing Series,1619-7127Machine learningNeural networks (Computer science) Machine Learninghttps://scigraph.springernature.com/ontologies/product-market-codes/I21010Mathematical Models of Cognitive Processes and Neural Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/M13100Machine learning.Neural networks (Computer science) .Machine Learning.Mathematical Models of Cognitive Processes and Neural Networks.006.32Iba Hitoshiedthttp://id.loc.gov/vocabulary/relators/edtNoman Nasimuledthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996465469103316Deep Neural Evolution2196251UNISA02911nam 22004693 450 991016331360332120230220084621.097817854359661785435965(CKB)3710000001046584(MiAaPQ)EBC7197452(Au-PeEL)EBL7197452(BIP)056086444(OCoLC)1370497297(EXLCZ)99371000000104658420230220d2016 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLoves Labours Lost “Beauty is bought by judgement of the eye.”1st ed.London :Copyright Group,2016.©2016.1 online resource (138 pages)The life of William Shakespeare, arguably the most significant figure in the Western literary canon, is relatively unknown. Shakespeare was born in Stratford-upon-Avon in 1565, possibly on the 23rd April, St. George's Day, and baptised there on 26th April. Little is known of his education and the first firm facts to his life relate to his marriage, aged 18, to Anne Hathaway, who was 26 and from the nearby village of Shottery. Anne gave birth to their first son six months later. Shakespeare's first play, The Comedy of Errors began a procession of real heavyweights that were to emanate from his pen in a career of just over twenty years in which 37 plays were written and his reputation forever established. This early skill was recognised by many and by 1594 the Lord Chamberlain's Men were performing his works. With the advantage of Shakespeare's progressive writing they rapidly became London's leading company of players, affording him more exposure and, following the death of Queen Elizabeth in 1603, a royal patent by the new king, James I, at which point they changed their name to the King's Men. By 1598, and despite efforts to pirate his work, Shakespeare's name was well known and had become a selling point in its own right on title pages. No plays are attributed to Shakespeare after 1613, and the last few plays he wrote before this time were in collaboration with other writers, one of whom is likely to be John Fletcher who succeeded him as the house playwright for the King's Men. William Shakespeare died two months later on April 23rd, 1616, survived by his wife, two daughters and a legacy of writing that none have since yet eclipsed.PrincessesNavarre (Kingdom)Courts and courtiersPrincesses.Navarre (Kingdom).Courts and courtiers.822.33Shakespeare Willam1276788MiAaPQMiAaPQMiAaPQBOOK9910163313603321Loves Labours Lost3010822UNINA