John MacCormick – författare
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9 produkter
9 produkter
Inbunden, Engelska, 2018
894 kr
Skickas inom 5-8 vardagar
An accessible and rigorous textbook for introducing undergraduates to computer science theoryWhat Can Be Computed? is a uniquely accessible yet rigorous introduction to the most profound ideas at the heart of computer science. Crafted specifically for undergraduates who are studying the subject for the first time, and requiring minimal prerequisites, the book focuses on the essential fundamentals of computer science theory and features a practical approach that uses real computer programs (Python and Java) and encourages active experimentation. It is also ideal for self-study and reference.The book covers the standard topics in the theory of computation, including Turing machines and finite automata, universal computation, nondeterminism, Turing and Karp reductions, undecidability, time-complexity classes such as P and NP, and NP-completeness, including the Cook-Levin Theorem. But the book also provides a broader view of computer science and its historical development, with discussions of Turing's original 1936 computing machines, the connections between undecidability and Gödel's incompleteness theorem, and Karp's famous set of twenty-one NP-complete problems.Throughout, the book recasts traditional computer science concepts by considering how computer programs are used to solve real problems. Standard theorems are stated and proven with full mathematical rigor, but motivation and understanding are enhanced by considering concrete implementations. The book's examples and other content allow readers to view demonstrations of—and to experiment with—a wide selection of the topics it covers. The result is an ideal text for an introduction to the theory of computation.An accessible and rigorous introduction to the essential fundamentals of computer science theory, written specifically for undergraduates taking introduction to the theory of computationFeatures a practical, interactive approach using real computer programs (Python in the text, with forthcoming Java alternatives online) to enhance motivation and understandingGives equal emphasis to computability and complexityIncludes special topics that demonstrate the profound nature of key ideas in the theory of computationLecture slides and Python programs are available at whatcanbecomputed.com
Inbunden, Engelska, 2026
253 kr
Skickas inom 5-8 vardagar
Can a computer program think like a human?“Can machines think?” Ever since Alan Turing posed this question in an influential 1950 paper, it has been central to research in artificial intelligence. More than seventy-five years after Turing’s paper, we grapple with it every time we wonder if Watson was actually smarter than Jeopardy! champions, or if ChatGPT really knows what it’s talking about. In Thinking AI, computer scientist John MacCormick explores Turing’s question from a perspective informed by a detailed understanding of the way modern AI systems work. MacCormick explains, in accessible fashion, the ideas behind the two main pillars of the twenty-first century AI revolution: deep neural networks and reinforcement learning.MacCormick offers a tour of the most famous AI systems, including AlexNet and VGG16, deep neural networks for object recognition that led to a Nobel prize; DeepMind’s AlphaGo, which shocked AI researchers with its superhuman performance in the game of Go; and OpenAI’s ChatGPT, which stunned the world with its natural language capabilities. He describes how each system works, and points to parallels with human brain processes. Both human minds and computer programs, MacCormick explains, can induce intelligence through emergence: the capability for new phenomena to emerge from the interactions of many small, simple components. Does this mean that a computer program can think like a human? In many ways, MacCormick argues, the answer is yes. In Thinking AI, he reveals a new landscape of emergent intelligence—a world in which computer programs can emulate many or all aspects of human thinking but humanity retains its meaning and purpose.
E-bok
PDF, Engelska, 2026281 kr
Läs direkt efter köp
Can a computer program think like a human?';Can machines think?' Ever since Alan Turing posed this question in an influential 1950 paper, it has been central to research in artificial intelligence. More than seventy-five years after Turing's paper, we grapple with it every time we wonder if Watson was actually smarter than Jeopardy! champions, or if ChatGPT really knows what it's talking about. In Thinking AI, computer scientist John MacCormick explores Turing's question from a perspective informed by a detailed understanding of the way modern AI systems work. MacCormick explains, in accessible fashion, the ideas behind the two main pillars of the twenty-first century AI revolution: deep neural networks and reinforcement learning.MacCormick offers a tour of the most famous AI systems, including AlexNet and VGG16, deep neural networks for object recognition that led to a Nobel prize; DeepMind's AlphaGo, which shocked AI researchers with its superhuman performance in the game of Go; and OpenAI's ChatGPT, which stunned the world with its natural language capabilities. He describes how each system works, and points to parallels with human brain processes. Both human minds and computer programs, MacCormick explains, can induce intelligence through emergence: the capability for new phenomena to emerge from the interactions of many small, simple components. Does this mean that a computer program can think like a human? In many ways, MacCormick argues, the answer is yes. In Thinking AI, he reveals a new landscape of emergent intelligencea world in which computer programs can emulate many or all aspects of human thinking but humanity retains its meaning and purpose.
E-bok
Engelska, 2020236 kr
Läs direkt efter köp
Nine revolutionary algorithms that power our computers and smartphonesEvery day, we use our computers to perform remarkable feats. A simple web search picks out a handful of relevant needles from the world''s biggest haystack. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers, and we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease? John MacCormick answers this question in language anyone can understand, using vivid examples to explain the fundamental tricks behind nine computer algorithms that power our PCs, tablets, and smartphones.
Häftad, Engelska, 2020
171 kr
Skickas inom 5-8 vardagar
Nine revolutionary algorithms that power our computers and smartphonesEvery day, we use our computers to perform remarkable feats. A simple web search picks out a handful of relevant needles from the world's biggest haystack. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers, and we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease? John MacCormick answers this question in language anyone can understand, using vivid examples to explain the fundamental tricks behind nine computer algorithms that power our PCs, tablets, and smartphones.
E-bok
Engelska, 2026281 kr
Läs direkt efter köp
Can a computer program think like a human?';Can machines think?' Ever since Alan Turing posed this question in an influential 1950 paper, it has been central to research in artificial intelligence. More than seventy-five years after Turing's paper, we grapple with it every time we wonder if Watson was actually smarter than Jeopardy! champions, or if ChatGPT really knows what it's talking about. In Thinking AI, computer scientist John MacCormick explores Turing's question from a perspective informed by a detailed understanding of the way modern AI systems work. MacCormick explains, in accessible fashion, the ideas behind the two main pillars of the twenty-first century AI revolution: deep neural networks and reinforcement learning.MacCormick offers a tour of the most famous AI systems, including AlexNet and VGG16, deep neural networks for object recognition that led to a Nobel prize; DeepMind's AlphaGo, which shocked AI researchers with its superhuman performance in the game of Go; and OpenAI's ChatGPT, which stunned the world with its natural language capabilities. He describes how each system works, and points to parallels with human brain processes. Both human minds and computer programs, MacCormick explains, can induce intelligence through emergence: the capability for new phenomena to emerge from the interactions of many small, simple components. Does this mean that a computer program can think like a human? In many ways, MacCormick argues, the answer is yes. In Thinking AI, he reveals a new landscape of emergent intelligencea world in which computer programs can emulate many or all aspects of human thinking but humanity retains its meaning and purpose.
E-bok
PDF, Engelska, 20181 255 kr
Läs direkt efter köp
An accessible and rigorous textbook for introducing undergraduates to computer science theoryWhat Can Be Computed? is a uniquely accessible yet rigorous introduction to the most profound ideas at the heart of computer science. Crafted specifically for undergraduates who are studying the subject for the first time, and requiring minimal prerequisites, the book focuses on the essential fundamentals of computer science theory and features a practical approach that uses real computer programs (Python and Java) and encourages active experimentation. It is also ideal for self-study and reference.The book covers the standard topics in the theory of computation, including Turing machines and finite automata, universal computation, nondeterminism, Turing and Karp reductions, undecidability, time-complexity classes such as P and NP, and NP-completeness, including the Cook-Levin Theorem. But the book also provides a broader view of computer science and its historical development, with discussions of Turing''s original 1936 computing machines, the connections between undecidability and Gödel''s incompleteness theorem, and Karp''s famous set of twenty-one NP-complete problems.Throughout, the book recasts traditional computer science concepts by considering how computer programs are used to solve real problems. Standard theorems are stated and proven with full mathematical rigor, but motivation and understanding are enhanced by considering concrete implementations. The book''s examples and other content allow readers to view demonstrations of—and to experiment with—a wide selection of the topics it covers. The result is an ideal text for an introduction to the theory of computation.
An accessible and rigorous introduction to the essential fundamentals of computer science theory, written specifically for undergraduates taking introduction to the theory of computationFeatures a practical, interactive approach using real computer programs (Python in the text, with forthcoming Java alternatives online) to enhance motivation and understandingGives equal emphasis to computability and complexityIncludes special topics that demonstrate the profound nature of key ideas in the theory of computationLecture slides and Python programs are available at whatcanbecomputed.com
E-bok
PDF, Engelska, 20121 459 kr
Läs direkt efter köp
A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer''s "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory.
Häftad, Engelska, 2011
1 109 kr
Skickas inom 10-15 vardagar
A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory.