Deep Learning Illustriert

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Deep Learning Illustrated

Author : Jon Krohn
Publisher : Addison-Wesley Professional
ISBN 13 : 0135121728
Total Pages : 725 pages
Book Rating : 4.1/5 (351 download)

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Book Synopsis Deep Learning Illustrated by : Jon Krohn

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Deep Learning

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262537559
Total Pages : 298 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Deep Learning by : John D. Kelleher

Download or read book Deep Learning written by John D. Kelleher and published by MIT Press. This book was released on 2019-09-10 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

Advanced Deep Learning for Engineers and Scientists

Author : Kolla Bhanu Prakash
Publisher : Springer Nature
ISBN 13 : 3030665194
Total Pages : 294 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Advanced Deep Learning for Engineers and Scientists by : Kolla Bhanu Prakash

Download or read book Advanced Deep Learning for Engineers and Scientists written by Kolla Bhanu Prakash and published by Springer Nature. This book was released on 2021-07-24 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.

Remote Sensing and GIS for Ecologists

Author : Martin Wegmann
Publisher : Pelagic Publishing Ltd
ISBN 13 : 1784270245
Total Pages : 410 pages
Book Rating : 4.7/5 (842 download)

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Book Synopsis Remote Sensing and GIS for Ecologists by : Martin Wegmann

Download or read book Remote Sensing and GIS for Ecologists written by Martin Wegmann and published by Pelagic Publishing Ltd. This book was released on 2016-02-08 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book about how ecologists can integrate remote sensing and GIS in their daily work. It will allow ecologists to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. All practical examples in this book rely on OpenSource software and freely available data sets. Quantum GIS (QGIS) is introduced for basic GIS data handling, and in-depth spatial analytics and statistics are conducted with the software packages R and GRASS. Readers will learn how to apply remote sensing within ecological research projects, how to approach spatial data sampling and how to interpret remote sensing derived products. The authors discuss a wide range of statistical analyses with regard to satellite data as well as specialised topics such as time-series analysis. Extended scripts on how to create professional looking maps and graphics are also provided. This book is a valuable resource for students and scientists in the fields of conservation and ecology interested in learning how to get started in applying remote sensing in ecological research and conservation planning.

Algorithm Design

Author : Michael T. Goodrich
Publisher : John Wiley & Sons
ISBN 13 : 0471383651
Total Pages : 722 pages
Book Rating : 4.4/5 (713 download)

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Book Synopsis Algorithm Design by : Michael T. Goodrich

Download or read book Algorithm Design written by Michael T. Goodrich and published by John Wiley & Sons. This book was released on 2001-10-15 with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt: Michael Goodrich and Roberto Tamassia, authors of the successful, Data Structures and Algorithms in Java, 2/e, have written Algorithm Engineering, a text designed to provide a comprehensive introduction to the design, implementation and analysis of computer algorithms and data structures from a modern perspective. This book offers theoretical analysis techniques as well as algorithmic design patterns and experimental methods for the engineering of algorithms. Market: Computer Scientists; Programmers.

Principles of Digital Image Processing

Author : Wilhelm Burger
Publisher : Springer Science & Business Media
ISBN 13 : 1848829191
Total Pages : 374 pages
Book Rating : 4.8/5 (488 download)

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Book Synopsis Principles of Digital Image Processing by : Wilhelm Burger

Download or read book Principles of Digital Image Processing written by Wilhelm Burger and published by Springer Science & Business Media. This book was released on 2013-11-18 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook is the third of three volumes which provide a modern, algorithmic introduction to digital image processing, designed to be used both by learners desiring a firm foundation on which to build, and practitioners in search of critical analysis and concrete implementations of the most important techniques. This volume builds upon the introductory material presented in the first two volumes with additional key concepts and methods in image processing. Features: practical examples and carefully constructed chapter-ending exercises; real implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitioners; easily adaptable Java code and completely worked-out examples for easy inclusion in existing applications; uses ImageJ; provides a supplementary website with the complete Java source code, test images, and corrections; additional presentation tools for instructors including a complete set of figures, tables, and mathematical elements.

Deep Learning illustriert

Author : Jon Krohn
Publisher : dpunkt.verlag
ISBN 13 : 3960887515
Total Pages : 472 pages
Book Rating : 4.9/5 (68 download)

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Book Synopsis Deep Learning illustriert by : Jon Krohn

Download or read book Deep Learning illustriert written by Jon Krohn and published by dpunkt.verlag. This book was released on 2020-09-08 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning begreifen und einsetzen Einführung in verwandte Themen wie Künstliche Intelligenz, Machine Learning und Neuronale Netze viele Illustrationen, verständlich erklärt begleitendes online-Material zum Ausprobieren der Erläuterungen aus dem Buch (Jupyter-Notebooks) Vorstellung von Bibliotheken (Tensor Flow/Keras, PyTorch) Deep Learning verändert unseren Alltag. Dieser Ansatz für maschinelles Lernen erzielt bahnbrechende Ergebnisse in einigen der bekanntesten Anwendungen von heute, in Unternehmen von Google bis Tesla, Facebook bis Apple. Tausende von technischen Fachkräften und Studenten wollen seine Möglichkeiten einsetzen, aber frühere Bücher über Deep Learning waren oft nicht intuitiv, unzugänglich und trocken. John Krohn, Grant Beylefeld und Aglaé Bassens bieten Ihnen eine einzigartige visuelle, intuitive und verständliche Einführung in Techniken und Anwendungen von Deep Learning. Mit den farbenfrohen Illustrationen und eingängigen Erläuterungen von "Deep Learning illustriert" gelingt Ihnen ein einfacher Zugang zum Aufbau von Deep-Learning-Modellen, und bringt ihnen beim Lernen mehr Spaß. Der erste Teil des Buches erklärt, was Deep Learning ist, warum es so allgegenwärtig geworden ist und wie es mit Konzepten und Terminologien wie künstlicher Intelligenz, Machine Learning oder künstlichen neuronalen Netzen interagiert. Dabei verwenden die Autoren leicht verständliche Analogien, lebendige Grafiken und viele Beispiele. Auf dieser Grundlage präsentieren die Autoren eine praktische Referenz und ein Tutorial zur Anwendung eines breiten Spektrums bewährter Techniken des Deep Learning. Die wesentliche Theorie wird mit so wenig Mathematik wie möglich behandelt und mit praktischem Python-Code beleuchtet. Praktische Beispiele zum Ausprobieren, die kostenfrei online verfügbar sind (Jupyter-Notebooks), machen Ihnen die Theorie begreiflich. So erlangen Sie ein pragmatisches Verständnis aller wichtigen Deep-Learning-Ansätze und ihrer Anwendungen: Machine Vision, Natural Language Processing, Bilderzeugung und Spielalgorithmen. Um Ihnen zu helfen, mehr in kürzerer Zeit zu erreichen, stellen die Autoren mehrere der heute am weitesten verbreiteten und innovativsten Deep-Learning-Bibliotheken vor, darunter: - TensorFlow und seine High-Level-API, Keras - PyTorch - High-Level-Coach, eine TensorFlow-API, die die Komplexität, die typischerweise mit der Entwicklung von Deep Reinforcement Learning-Algorithmen verbunden ist, abstrahiert.

Nonlinear Signal Processing

Author : Gonzalo R. Arce
Publisher : John Wiley & Sons
ISBN 13 : 0471691844
Total Pages : 483 pages
Book Rating : 4.4/5 (716 download)

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Book Synopsis Nonlinear Signal Processing by : Gonzalo R. Arce

Download or read book Nonlinear Signal Processing written by Gonzalo R. Arce and published by John Wiley & Sons. This book was released on 2005-01-03 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two non-Gaussian models, a large set of tools is developed that encompass a large portion of the nonlinear signal processing tools proposed in the literature over the past several decades. Key features include: * Numerous problems at the end of each chapter to aid development and understanding * Examples and case studies provided throughout the book in a wide range of applications bring the text to life and place the theory into context * A set of 60+ MATLAB software m-files allowing the reader to quickly design and apply any of the nonlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site.

Deep Learning

Author : Andrew Glassner
Publisher : No Starch Press
ISBN 13 : 1718500734
Total Pages : 1315 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis Deep Learning by : Andrew Glassner

Download or read book Deep Learning written by Andrew Glassner and published by No Starch Press. This book was released on 2021-06-22 with total page 1315 pages. Available in PDF, EPUB and Kindle. Book excerpt: A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations

Designing Data-Intensive Applications

Author : Martin Kleppmann
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491903104
Total Pages : 658 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Designing Data-Intensive Applications by : Martin Kleppmann

Download or read book Designing Data-Intensive Applications written by Martin Kleppmann and published by "O'Reilly Media, Inc.". This book was released on 2017-03-16 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

PMP Certification All-In-One Desk Reference For Dummies

Author : Cynthia Stackpole
Publisher : John Wiley & Sons
ISBN 13 : 0470087153
Total Pages : 866 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis PMP Certification All-In-One Desk Reference For Dummies by : Cynthia Stackpole

Download or read book PMP Certification All-In-One Desk Reference For Dummies written by Cynthia Stackpole and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bonus CD-ROM includes Dummies Test Engine, an exclusive practice exam with hundreds of sample questions based on the actual exam.

Deep Learning Essentials

Author : Anurag Bhardwaj
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887777
Total Pages : 271 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Deep Learning Essentials by : Anurag Bhardwaj

Download or read book Deep Learning Essentials written by Anurag Bhardwaj and published by Packt Publishing Ltd. This book was released on 2018-01-30 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Frequently Asked Questions about the Universe

Author : Jorge Cham
Publisher : Penguin
ISBN 13 : 0593189310
Total Pages : 337 pages
Book Rating : 4.5/5 (931 download)

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Book Synopsis Frequently Asked Questions about the Universe by : Jorge Cham

Download or read book Frequently Asked Questions about the Universe written by Jorge Cham and published by Penguin. This book was released on 2021-11-02 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Delightful, funny, and yet rigorous and intelligent: only Jorge and Daniel can reach this exquisite balance." —Carlo Rovelli, author of Seven Brief Lessons on Physics and Helgoland You’ve got questions: about space, time, gravity, and the odds of meeting your older self inside a wormhole. All the answers you need are right here. As a species, we may not agree on much, but one thing brings us all together: a need to know. We all wonder, and deep down we all have the same big questions. Why can’t I travel back in time? Where did the universe come from? What’s inside a black hole? Can I rearrange the particles in my cat and turn it into a dog? Researcher-turned-cartoonist Jorge Cham and physics professor Daniel Whiteson are experts at explaining science in ways we can all understand, in their books and on their popular podcast, Daniel and Jorge Explain the Universe. With their signature blend of humor and oh-now-I-get-it clarity, Jorge and Daniel offer short, accessible, and lighthearted answers to some of the most common, most outrageous, and most profound questions about the universe they’ve received. This witty, entertaining, and fully illustrated book is an essential troubleshooting guide for the perplexing aspects of reality, big and small, from the invisible particles that make up your body to the identical version of you currently reading this exact sentence in the corner of some other galaxy. If the universe came with an FAQ, this would be it.

Introduction to Deep Learning

Author : Eugene Charniak
Publisher : MIT Press
ISBN 13 : 0262039516
Total Pages : 187 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Introduction to Deep Learning by : Eugene Charniak

Download or read book Introduction to Deep Learning written by Eugene Charniak and published by MIT Press. This book was released on 2019-01-29 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Art & Max

Author : David Wiesner
Publisher : HarperCollins
ISBN 13 : 0547505906
Total Pages : 45 pages
Book Rating : 4.5/5 (475 download)

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Book Synopsis Art & Max by : David Wiesner

Download or read book Art & Max written by David Wiesner and published by HarperCollins. This book was released on 2010-10-04 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Max and Arthur are friends who share an interest in painting. Arthur is an accomplished painter; Max is a beginner. Max’s first attempt at using a paintbrush sends the two friends on a whirlwind trip through various artistic media, which turn out to have unexpected pitfalls. Although Max is inexperienced, he’s courageous—and a quick learner. His energy and enthusiasm bring the adventure to its triumphant conclusion. Beginners everywhere will take heart.

Deep Learning For Dummies

Author : John Paul Mueller
Publisher : John Wiley & Sons
ISBN 13 : 1119543045
Total Pages : 370 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Deep Learning For Dummies by : John Paul Mueller

Download or read book Deep Learning For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2019-05-14 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Universal Artificial Intelligence

Author : Marcus Hutter
Publisher : Springer Science & Business Media
ISBN 13 : 3540268774
Total Pages : 294 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Universal Artificial Intelligence by : Marcus Hutter

Download or read book Universal Artificial Intelligence written by Marcus Hutter and published by Springer Science & Business Media. This book was released on 2005-12-29 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.