Evolutionary Approach To Machine Learning And Deep Neural Networks

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Evolutionary Approach to Machine Learning and Deep Neural Networks

Author : Hitoshi Iba
Publisher : Springer
ISBN 13 : 9811302006
Total Pages : 254 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Evolutionary Approach to Machine Learning and Deep Neural Networks by : Hitoshi Iba

Download or read book Evolutionary Approach to Machine Learning and Deep Neural Networks written by Hitoshi Iba and published by Springer. This book was released on 2018-06-15 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields. Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution. The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.

Evolutionary Algorithms and Neural Networks

Author : Seyedali Mirjalili
Publisher : Springer
ISBN 13 : 3319930257
Total Pages : 164 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Evolutionary Algorithms and Neural Networks by : Seyedali Mirjalili

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 164 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.

Deep Neural Evolution

Author : Hitoshi Iba
Publisher : Springer Nature
ISBN 13 : 9811536856
Total Pages : 437 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Deep Neural Evolution by : Hitoshi Iba

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.

Evolutionary Machine Learning Techniques

Author : Seyedali Mirjalili
Publisher : Springer Nature
ISBN 13 : 9813299908
Total Pages : 287 pages
Book Rating : 4.8/5 (132 download)

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Book Synopsis Evolutionary Machine Learning Techniques by : Seyedali Mirjalili

Download or read book Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and published by Springer Nature. This book was released on 2019-11-11 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.

The Master Algorithm

Author : Pedro Domingos
Publisher : Basic Books
ISBN 13 : 0465061923
Total Pages : 354 pages
Book Rating : 4.4/5 (65 download)

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Book Synopsis The Master Algorithm by : Pedro Domingos

Download or read book The Master Algorithm written by Pedro Domingos and published by Basic Books. This book was released on 2015-09-22 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

Genetic Programming for Image Classification

Author : Ying Bi
Publisher : Springer Nature
ISBN 13 : 3030659275
Total Pages : 279 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Genetic Programming for Image Classification by : Ying Bi

Download or read book Genetic Programming for Image Classification written by Ying Bi and published by Springer Nature. This book was released on 2021-02-08 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.

The Deep Learning Revolution

Author : Terrence J. Sejnowski
Publisher : MIT Press
ISBN 13 : 026203803X
Total Pages : 354 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis The Deep Learning Revolution by : Terrence J. Sejnowski

Download or read book The Deep Learning Revolution written by Terrence J. Sejnowski and published by MIT Press. This book was released on 2018-10-23 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

An Introduction to Genetic Algorithms

Author : Melanie Mitchell
Publisher : MIT Press
ISBN 13 : 9780262631853
Total Pages : 226 pages
Book Rating : 4.6/5 (318 download)

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Book Synopsis An Introduction to Genetic Algorithms by : Melanie Mitchell

Download or read book An Introduction to Genetic Algorithms written by Melanie Mitchell and published by MIT Press. This book was released on 1998-03-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Artificial Intelligence in the Age of Neural Networks and Brain Computing

Author : Robert Kozma
Publisher : Academic Press
ISBN 13 : 0323958168
Total Pages : 398 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Artificial Intelligence in the Age of Neural Networks and Brain Computing by : Robert Kozma

Download or read book Artificial Intelligence in the Age of Neural Networks and Brain Computing written by Robert Kozma and published by Academic Press. This book was released on 2023-10-11 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. - Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN - Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making - Edited by high-level academics and researchers in intelligent systems and neural networks - Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Blockchain and Deep Learning

Author : Khaled R. Ahmed
Publisher : Springer Nature
ISBN 13 : 3030954196
Total Pages : 352 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Blockchain and Deep Learning by : Khaled R. Ahmed

Download or read book Blockchain and Deep Learning written by Khaled R. Ahmed and published by Springer Nature. This book was released on 2022-03-25 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces to blockchain and deep learning and explores and illustrates the current and new trends that integrate them. The pace and speeds for connectivity are certain on the ascend. Blockchain and deep learning are twin technologies that are integral to integrity and relevance of network contents. Since they are data-driven technologies, rapidly growing interests exist to incorporate them in efficient and secure data sharing and analysis applications. Blockchain and deep learning are sentinel contemporary research technologies. This book provides a comprehensive reference for blockchain and deep learning by covering all important topics. It identifies the bedrock principles and forward projecting methodologies that illuminate the trajectory of developments for the decades ahead.

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Automated Machine Learning by : Frank Hutter

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.

Advanced Machine Learning with Evolutionary and Metaheuristic Techniques

Author : Jayaraman Valadi
Publisher : Springer Nature
ISBN 13 : 9819997186
Total Pages : 365 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Advanced Machine Learning with Evolutionary and Metaheuristic Techniques by : Jayaraman Valadi

Download or read book Advanced Machine Learning with Evolutionary and Metaheuristic Techniques written by Jayaraman Valadi and published by Springer Nature. This book was released on with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices

Author : Hamido Fujita
Publisher : Springer Nature
ISBN 13 : 3030557898
Total Pages : 931 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices by : Hamido Fujita

Download or read book Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices written by Hamido Fujita and published by Springer Nature. This book was released on 2020-09-04 with total page 931 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, held in Kitakyushu, Japan, in September 2020. The 62 full papers and 17 short papers presented were carefully reviewed and selected from 119 submissions. The IEA/AIE 2020 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include are language processing; robotics and drones; knowledge based systems; innovative applications of intelligent systems; industrial applications; networking applications; social network analysis; financial applications and blockchain; medical and health-related applications; anomaly detection and automated diagnosis; decision-support and agent-based systems; multimedia applications; machine learning; data management and data clustering; pattern mining; system control, classification, and fault diagnosis.

AI and SWARM

Author : Hitoshi Iba
Publisher : CRC Press
ISBN 13 : 042964843X
Total Pages : 218 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis AI and SWARM by : Hitoshi Iba

Download or read book AI and SWARM written by Hitoshi Iba and published by CRC Press. This book was released on 2019-09-17 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides theoretical and practical knowledge on AI and swarm intelligence. It provides a methodology for EA (evolutionary algorithm)-based approach for complex adaptive systems with the integration of several meta-heuristics, e.g., ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), and PSO (Particle Swarm Optimization), etc. These developments contribute towards better problem-solving methodologies in AI. The book also covers emerging uses of swarm intelligence in applications such as complex adaptive systems, reaction-diffusion computing, and diffusion-limited aggregation, etc. Another emphasis is its real-world applications. We give empirical examples from real-world problems and show that the proposed approaches are successful when addressing tasks from such areas as swarm robotics, silicon traffics, image understanding, Vornoi diagrams, queuing theory, and slime intelligence, etc. Each chapter begins with the background of the problem followed by the current state-of-the-art techniques of the field, and ends with a detailed discussion. In addition, the simulators, based on optimizers such as PSO and ABC complex adaptive system simulation, are described in detail. These simulators, as well as some source codes, are available online on the author’s website for the benefit of readers interested in getting some hands-on experience of the subject. The concepts presented in this book aim to promote and facilitate the effective research in swarm intelligence approaches in both theory and practice. This book would also be of value to other readers because it covers interdisciplinary research topics that encompass problem-solving tasks in AI, complex adaptive systems, and meta-heuristics.

Introduction To Evolutionary Informatics

Author : Robert J Marks Ii
Publisher : World Scientific
ISBN 13 : 9813142162
Total Pages : 331 pages
Book Rating : 4.8/5 (131 download)

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Book Synopsis Introduction To Evolutionary Informatics by : Robert J Marks Ii

Download or read book Introduction To Evolutionary Informatics written by Robert J Marks Ii and published by World Scientific. This book was released on 2017-02-27 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Science has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature.Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information.This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory.

Hands-On Neuroevolution with Python

Author : Iaroslav Omelianenko
Publisher : Packt Publishing Ltd
ISBN 13 : 1838822003
Total Pages : 359 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Hands-On Neuroevolution with Python by : Iaroslav Omelianenko

Download or read book Hands-On Neuroevolution with Python written by Iaroslav Omelianenko and published by Packt Publishing Ltd. This book was released on 2019-12-24 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution Key FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook Description Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments. What you will learnDiscover the most popular neuroevolution algorithms – NEAT, HyperNEAT, and ES-HyperNEATExplore how to implement neuroevolution-based algorithms in PythonGet up to speed with advanced visualization tools to examine evolved neural network graphsUnderstand how to examine the results of experiments and analyze algorithm performanceDelve into neuroevolution techniques to improve the performance of existing methodsApply deep neuroevolution to develop agents for playing Atari gamesWho this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.

Machine Learning with Neural Networks

Author : Bernhard Mehlig
Publisher : Cambridge University Press
ISBN 13 : 1108849563
Total Pages : 262 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Machine Learning with Neural Networks by : Bernhard Mehlig

Download or read book Machine Learning with Neural Networks written by Bernhard Mehlig and published by Cambridge University Press. This book was released on 2021-10-28 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.