Dominant Algorithms To Evaluate Artificial Intelligence From The View Of Throughput Model

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Dominant Algorithms to Evaluate Artificial Intelligence: From the View of Throughput Model

Author : Waymond Rodgers
Publisher : Bentham Science Publishers
ISBN 13 : 9815049550
Total Pages : 329 pages
Book Rating : 4.8/5 (15 download)

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Book Synopsis Dominant Algorithms to Evaluate Artificial Intelligence: From the View of Throughput Model by : Waymond Rodgers

Download or read book Dominant Algorithms to Evaluate Artificial Intelligence: From the View of Throughput Model written by Waymond Rodgers and published by Bentham Science Publishers. This book was released on 2022-07-20 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the Throughput Model methodology that can enable individuals and organizations to better identify, understand, and use algorithms to solve daily problems. The Throughput Model is a progressive model intended to advance the artificial intelligence (AI) field since it represents symbol manipulation in six algorithmic pathways that are theorized to mimic the essential pillars of human cognition, namely, perception, information, judgment, and decision choice. The six AI algorithmic pathways are (1) Expedient Algorithmic Pathway, (2) Ruling Algorithmic Guide Pathway, (3) Analytical Algorithmic Pathway, (4) Revisionist Algorithmic Pathway, (5) Value Driven Algorithmic Pathway, and (6) Global Perspective Algorithmic Pathway. As AI is increasingly employed for applications where decisions require explanations, the Throughput Model offers business professionals the means to look under the hood of AI and comprehend how those decisions are attained by organizations. Key Features: - Covers general concepts of Artificial intelligence and machine learning - Explains the importance of dominant AI algorithms for business and AI research - Provides information about 6 unique algorithmic pathways in the Throughput Model - Provides information to create a roadmap towards building architectures that combine the strengths of the symbolic approaches for analyzing big data - Explains how to understand the functions of an AI algorithm to solve problems and make good decisions - informs managers who are interested in employing ethical and trustworthiness features in systems. Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model is an informative reference for all professionals and scholars who are working on AI projects to solve a range of business and technical problems.

Artificial Intelligence in Medical Imaging

Author : Erik R. Ranschaert
Publisher : Springer
ISBN 13 : 3319948784
Total Pages : 373 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Artificial Intelligence in Medical Imaging by : Erik R. Ranschaert

Download or read book Artificial Intelligence in Medical Imaging written by Erik R. Ranschaert and published by Springer. This book was released on 2019-01-29 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Genetic Algorithms in Search, Optimization, and Machine Learning

Author : David Edward Goldberg
Publisher : Addison-Wesley Professional
ISBN 13 :
Total Pages : 436 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Genetic Algorithms in Search, Optimization, and Machine Learning by : David Edward Goldberg

Download or read book Genetic Algorithms in Search, Optimization, and Machine Learning written by David Edward Goldberg and published by Addison-Wesley Professional. This book was released on 1989 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.

Machine Learning Algorithms

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1785884514
Total Pages : 360 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Machine Learning Algorithms by : Giuseppe Bonaccorso

Download or read book Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

An Introduction to Machine Learning

Author : Gopinath Rebala
Publisher : Springer
ISBN 13 : 3030157296
Total Pages : 263 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis An Introduction to Machine Learning by : Gopinath Rebala

Download or read book An Introduction to Machine Learning written by Gopinath Rebala and published by Springer. This book was released on 2019-05-07 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.

Multi-Objective Optimization using Artificial Intelligence Techniques

Author : Seyedali Mirjalili
Publisher : Springer
ISBN 13 : 3030248356
Total Pages : 58 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Multi-Objective Optimization using Artificial Intelligence Techniques by : Seyedali Mirjalili

Download or read book Multi-Objective Optimization using Artificial Intelligence Techniques written by Seyedali Mirjalili and published by Springer. This book was released on 2019-07-24 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Artificial Intelligence

Author : David L. Poole
Publisher : Cambridge University Press
ISBN 13 : 110719539X
Total Pages : 821 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Artificial Intelligence by : David L. Poole

Download or read book Artificial Intelligence written by David L. Poole and published by Cambridge University Press. This book was released on 2017-09-25 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Ant Colony Optimization

Author : Marco Dorigo
Publisher : MIT Press
ISBN 13 : 9780262042192
Total Pages : 324 pages
Book Rating : 4.0/5 (421 download)

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Book Synopsis Ant Colony Optimization by : Marco Dorigo

Download or read book Ant Colony Optimization written by Marco Dorigo and published by MIT Press. This book was released on 2004-06-04 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

Efficient Learning Machines

Author : Mariette Awad
Publisher : Apress
ISBN 13 : 1430259906
Total Pages : 263 pages
Book Rating : 4.4/5 (32 download)

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Book Synopsis Efficient Learning Machines by : Mariette Awad

Download or read book Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 1000730778
Total Pages : 538 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

MATLAB for Machine Learning

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1788399390
Total Pages : 374 pages
Book Rating : 4.7/5 (883 download)

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Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro

Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Dive Into Deep Learning

Author : Joanne Quinn
Publisher : Corwin Press
ISBN 13 : 1544385404
Total Pages : 297 pages
Book Rating : 4.5/5 (443 download)

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Book Synopsis Dive Into Deep Learning by : Joanne Quinn

Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.

Braunwald's Heart Disease - E-Book

Author : Peter Libby
Publisher : Elsevier Health Sciences
ISBN 13 : 0323824714
Total Pages : 2473 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Braunwald's Heart Disease - E-Book by : Peter Libby

Download or read book Braunwald's Heart Disease - E-Book written by Peter Libby and published by Elsevier Health Sciences. This book was released on 2021-10-15 with total page 2473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current, comprehensive, and evidence-based Braunwald’s Heart Disease remains the most trusted reference in the field and the leading source of reliable cardiology information for practitioners and trainees worldwide. The fully updated 12th Edition continues the tradition of excellence with dependable, state-of-the-art coverage of new drugs, new guidelines, more powerful imaging modalities, and recent developments in precision medicine that continue to change and advance the practice of cardiovascular medicine. Written and edited by global experts in the field, this award-winning text is an unparalleled multimedia reference for every aspect of this complex and fast-changing area. Offers balanced, dependable content on rapidly changing clinical science, clinical and translational research, and evidence-based medicine. Includes 76 new contributing authors and 14 new chapters that cover Artificial intelligence in Cardiovascular Medicine; Wearables; Influenza, Pandemics, COVID-19, and Cardiovascular Disease; Tobacco and Nicotine Products in Cardiovascular Disease; Cardiac Amyloidosis; Impact of the Environment on Cardiovascular Health, and more. Features a new introductory chapter Cardiovascular Disease: Past, Present, and Future by Eugene Braunwald, MD, offering his unique, visionary approach to the field of cardiology. Dr. Braunwald also curates the extensive, bimonthly online updates that include "Hot Off the Press" (with links to Practice Update) and "Late-Breaking Clinical Trials". Provides cutting-edge coverage of key topics such as proteomics and metabolomics, TAVR, diabetocardiology, and cardio-oncology. Contains 1,850 high-quality illustrations, radiographic images, algorithms, and charts, and provides access to 215 videos called out with icons in the print version. Highlights the latest AHA, ACC, and ESC guidelines to clearly summarize diagnostic criteria and clinical implications. Provides tightly edited, focused content for quick, dependable reference. Flexible format options include either one or two volumes in print, as well as a searchable eBook with ongoing updates.

Flying Ad Hoc Networks

Author : Jingjing Wang
Publisher : Springer Nature
ISBN 13 : 9811688508
Total Pages : 297 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Flying Ad Hoc Networks by : Jingjing Wang

Download or read book Flying Ad Hoc Networks written by Jingjing Wang and published by Springer Nature. This book was released on 2022-02-10 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Relying on unmanned autonomous flight control programs, unmanned aerial vehicles (UAVs) equipped with radio communication devices have been actively developed around the world. Given their low cost, flexible maneuvering and unmanned operation, UAVs have been widely used in both civilian operations and military missions, including environmental monitoring, emergency communications, express distribution, even military surveillance and attacks, for example. Given that a range of standards and protocols used in terrestrial wireless networks are not applicable to UAV networks, and that some practical constraints such as battery power and no-fly zone hinder the maneuverability capability of a single UAV, we need to explore advanced communication and networking theories and methods for the sake of supporting future ultra-reliable and low-latency applications. Typically, the full potential of UAV network’s functionalities can be tapped with the aid of the cooperation of multiple drones relying on their ad hoc networking, in-network communications and coordinated control. Furthermore, some swarm intelligence models and algorithms conceived for dynamic negotiation, path programming, formation flight and task assignment of multiple cooperative drones are also beneficial in terms of extending UAV’s functionalities and coverage, as well as of increasing their efficiency. We call the networking and cooperation of multiple drones as the terminology ‘flying ad hoc network (FANET)’, and there indeed are numerous new challenges to be overcome before the idespread of so-called heterogeneous FANETs. In this book, we examine a range of technical issues in FANETs, from physical-layer channel modeling to MAC-layer resource allocation, while also introducing readers to UAV aided mobile edge computing techniques.

Machine Learning in Finance

Author : Matthew F. Dixon
Publisher : Springer Nature
ISBN 13 : 3030410684
Total Pages : 565 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Machine Learning in Finance by : Matthew F. Dixon

Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Introduction to Evolutionary Computing

Author : Agoston E. Eiben
Publisher : Springer Science & Business Media
ISBN 13 : 3662050943
Total Pages : 307 pages
Book Rating : 4.6/5 (62 download)

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Book Synopsis Introduction to Evolutionary Computing by : Agoston E. Eiben

Download or read book Introduction to Evolutionary Computing written by Agoston E. Eiben and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

Architects of Intelligence

Author : Martin Ford
Publisher : Packt Publishing Ltd
ISBN 13 : 178913126X
Total Pages : 540 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Architects of Intelligence by : Martin Ford

Download or read book Architects of Intelligence written by Martin Ford and published by Packt Publishing Ltd. This book was released on 2018-11-23 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Times Best Books of the Year 2018 TechRepublic Top Books Every Techie Should Read Book Description How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community. Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel). Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future. Meet the minds behind the AI superpowers as they discuss the science, business and ethics of modern artificial intelligence. Read James Manyika’s thoughts on AI analytics, Geoffrey Hinton’s breakthroughs in AI programming and development, and Rana el Kaliouby’s insights into AI marketing. This AI book collects the opinions of the luminaries of the AI business, such as Stuart Russell (coauthor of the leading AI textbook), Rodney Brooks (a leader in AI robotics), Demis Hassabis (chess prodigy and mind behind AlphaGo), and Yoshua Bengio (leader in deep learning) to complete your AI education and give you an AI advantage in 2019 and the future.