john kelleher (331 résultats)

Paperback. Etat : Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Etat : very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.1.

Etat : Good. Ships from the UK. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

Etat : Very Good. 1757586633. 9/11/2025 10:30:33 AM.

Paperback. Etat : Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.58.

Paperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.58.

Paperback. Etat : Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.58.

Paperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.58.

Paperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.58.

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.58.

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.58.

Etat : Good. 1st Edition. Ships from the UK. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

Etat : acceptable. Ausreichend/Acceptable: Exemplar mit vollständigem Text und sämtlichen Abbildungen oder Karten. Schmutztitel oder Vorsatz können fehlen. Einband bzw. Schutzumschlag weisen unter Umständen starke Gebrauchsspuren auf. / Describes a book or dust jacket that has the complete text pages (including those with maps or plates) but may lack endpapers, half-title, etc. (which must be noted). Binding, dust jacket (if any), etc may also be worn.

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.6.

Etat : good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Etat : very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Etat : Very Good. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.

Etat : Good. Used book that is in clean, average condition without any missing pages.

Etat : Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.

Paperback. Etat : Very Good.

Paperback. Etat : Good. Illustrated. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

Paperback. Etat : Near New. 157 pp. Near new with relevant press clippings loosely inserted. Signed by the Author.

Paperback. Etat : New. 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.

Paperback. Etat : New. A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Etat : Very Good. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.

Etat : Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.

Paperback. Etat : Very Good. A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Paperback. Etat : New. 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.

Paperback. Etat : New. A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.