Neural networks

Springer, 2022. — 528 p. — ISBN: 978-3-030-92525-3. This book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to...

Springer International Publishing AG, 2017. — 457 p. — (Texts in Applied Mathematics 66) — ISBN-10 331951170X. This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction...

3rd edition. — Humana Press, 2021. — 368 p. — ISBN: 978-1-0716-0825-8. This volume presents examples of how Artificial Neural Networks (ANNs) are applied in biological sciences and related areas. Chapters cover a wide variety of topics, including the analysis of intracellular sorting information, prediction of the behavior of bacterial communities, biometric authentication,...

De Gruyter, 2022. — 174 p. — (De Gruyter Series in Applied and Numerical Mathematics 06). — 978-3-11-078318-6. Artificial Intelligence, Deep Learning, Machine Learning - whatever you’re doing if you don’t understand it - learn it. Because otherwise, you’re going to be a dinosaur within 3 years. This book uses numerical analysis as the main tool to investigate methods in Machine...

Packt Publishing, 2023. — 196 p. — ISBN: 978-1804617625. Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI and the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You'll begin by exploring...

Apress Media, LLC., 2023. — 223 p. — ISBN-13: 978-1-4842-8998-3. Demystify the creation of efficient AI systems using the model-based reinforcement learning Unity ML-Agents - a powerful bridge between the world of Unity and Python. We will start with an introduction to the field of AI, then discuss the progression of AI and where we are today. We will follow this up with a...

Springer, 2018. — 544 p. — (Smart Innovation, Systems and Technologies). — ISBN10: 3319950975, 13 978-3319950976. This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generation, filtering, equalization, signals from arrays of sensors,...

Amazon Digital Services LLC, 2018. — 128 p. — ASIN B07GF5KP9R This book is an introduction to Intent Based Networking and how your business can leverage many of the benefits that it realises through network modernisation, optimisation and business alignment. Organisations around the world of all sizes are having to adjust to doing business globally. But one of the biggest...

Springer International Publishing AG, 2017. — 90 p. — ISBN: 978-3-319-67770-5. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while...

Cybellium Ltd, 2023-12-02. — 323 p. — ASIN: B0CPD5FQTV. Unleash the Power of Deep Learning for Intelligent Systems. In the realm of artificial intelligence and machine learning, neural networks stand as the driving force behind intelligent systems that mimic human cognition. "Mastering Neural Networks" is your ultimate guide to comprehending and harnessing the potential of...

Princeton University Press, 2021. — 368 p. — ISBN: 978-0691181226. What neurobiology and artificial intelligence tell us about how the brain builds itself How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The...

Apress Media, 2025. — 191 p. — ISBN-13: 979-8-8688-1020-6. Explore the capabilities of Machine Learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, Machine Learning (ML) techniques, and large language models (LLMs). The book explores the core of Machine Learning techniques,...

Springer, 2018. — 245 p. — ISBN: 978-9811301995. This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting...

EHGBooks, 2018. — 397 p. — ASIN B07FHYXDYG. This book provides frequently studied and used machines together with soft computing methods such as evolutionary computation. The main topics of the machine learning cover Artificial Neural Networks (ANNs), Radial Basis Function Networks (RBFNs), Fuzzy Neural Networks (FNNs), Support Vector Machines (SVMs), and Wilcoxon Learning...

IGI Global, 2023. — 267 p. Graph Neural Networks, also known as GNNs, have seen a meteoric rise in popularity over the past few years due to their capacity to analyze data that is shown in the form of graphs. GNNs have been put to use in a broad variety of industries, including social network research, the search for new drugs, recommender systems, and traffic prediction, to...

2nd Edition. — Apress Media LLC, 2022. — 640 p. — ISBN-13 (electronic): 978-1-4842-7368-5. Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and backpropagation and helps you...

Apress, 2019. — 640 p. — ISBN-13 (electronic): 978-1-4842-4421-0. Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of...

Packt Publishing Ltd., 2019. — 385 p. — ISBN: 978-1-78913-890-0. Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis,...

Apress, 2018. — 139 p. — ISBN-13 9781484235065. Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you'll build an electronics project, providing a hands-on experience in training a network. There are no prerequisites here and you won't see a single line of computer code in this book....

Springer, 2024. — 204 p. — (Studies in Computational Intelligence 1146). — ISBN: 978-3-031-53712-7. We describe in this book new directions on the theoretical developments of fuzzy logic, neural networks, and meta-heuristic algorithms, as well as their hybrid combinations, and their application in areas such as intelligent control and robotics, pattern recognition, medical...

Wiley, 2023. — – 243 p. — ISBN: 9781119901990. Systems Engineering Neural Networks a complete and authoritative discussion of systems engineering and neural networks. In Systems Engineering Neural Networks, a team of distinguished researchers delivers a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems...

Packt Publishing, 2018. — 218 p. — ISBN: 978-1-78839-230-3. Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision...

Prentice-Hall, 2017. — 572 p. — ISBN13: 978-8120353343. This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as...

CreateSpace, 2016. — 222 p. A gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun...

The MIT Press, 2024. — 168 p. — ISBN: 978-0262378758. An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires...

CreateSpace Independent Publishing, 2018. — 114 p. — ISBN: 171759445X. Are you looking to get a better understanding of neural networks and their applications? Neural networks are used to model complex patterns for prediction and simulation. It uses the processing pattern used by brain neurons to achieve this. Neural Networks are good at processing complex , non-linear...

Blue Windmill Media, 2017. — 248 p. Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning? On a high level, a network learns just like we do, through trial and error. This is...

O’Reilly Media, 2019. — 228 p. — ISBN: 978-1-492-04495-6. As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately “fool” them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs — the algorithms intrinsic to much of AI — are used daily to...

Wiley-VCH, 2023. — 435 p. — ISBN: 978-1-119-98599-0. Zeroing Neural Networks. Describes the theoretical and practical aspects of finite-time ZNN methods for solving an array of computational problems. Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven time-varying matrix problems in engineering, control theory, and on-chip...

Berlin/Boston: De Gruyter, 2018. — 296 p. — ISBN: 3110449625. The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential...

Springer, 2024. — 439 p. — (Springer Series on Bio- and Neurosystems 16). — ISBN: 978-3-031-36705-2. This book offers a timely and comprehensive review of the field of neurotronics. Gathering cutting-edge contributions from neuroscientists, biologists, psychologists, as well as physicists, microelectronics engineers, and information scientists, it gives extensive information on...