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  • 1
    Book
    Book
    Cambridge, Massachusetts ; London, England :The MIT Press,
    UID:
    almafu_BV044714584
    Format: xi, 264 Seiten : , Illustrationen, Diagramme.
    ISBN: 978-0-262-53543-4 , 0-262-53543-2
    Series Statement: The MIT Press essential knowledge series
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-0-262-34702-0
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    RVK:
    RVK:
    Keywords: Data Science ; Big Data ; Data Science ; Maschinelles Lernen ; Einführung ; Einführung ; Handbooks and manuals ; Einführung
    URL: Cover
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Cambridge, MA ; London, England :The MIT Press,
    UID:
    almafu_BV046153971
    Format: x, 280 Seiten : , Illustrationen.
    ISBN: 978-0-262-53755-1
    Series Statement: The MIT press essential knowledge series
    Content: Introduction to deep learning -- Conceptual foundations -- Neural networks: the building blocks of deep learning -- A brief history of deep learning -- Convolutional and recurrent networks -- Learning functions -- The future of deep learning
    Content: "Artificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing an learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars. It is set to have a massive impact in healthcare, finance, and smart cities over the next years. This book is designed to give an accessible and concise, but also comprehensive, introduction to the field of Deep Learning. The book explains what deep learning is, how the field has developed, what deep learning can do, and also discusses how the field is likely to develop in the next 10 years. Along the way, the most important neural network architectures are described, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and more recent developments such as Generative Adversarial Networks, transformer networks, and capsule networks. The book also covers the two more important algorithms for training a neural network, the gradient descent algorithm and Backpropagation"--
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-0-262-35489-9
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen
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  • 3
  • 4
    Online Resource
    Online Resource
    Cambridge, Massachusetts : The MIT Press
    UID:
    gbv_1753572975
    Format: 1 Online-Ressource (xi, 264 Seiten) , Diagramme
    ISBN: 9780262347020
    Series Statement: The MIT Press essential knowledge series
    Content: 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. Einführung in das Gebiet der Datenwissenschaft.
    Note: Includes bibliographical references and index
    Additional Edition: ISBN 9780262535434
    Additional Edition: Erscheint auch als Druck-Ausgabe Kelleher, John D., 1974 - Data science Cambridge, Massachusetts : The MIT Press, 2018 ISBN 9780262535434
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    Keywords: Data Science ; Big Data ; Maschinelles Lernen ; Data Science ; Einführung
    URL: Cover
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Online Resource
    Online Resource
    Cambridge, MA ; London, England :The MIT Press,
    UID:
    edocfu_BV046934227
    Format: 1 Online-Ressource (x, 280 Seiten) : , Illustrationen.
    ISBN: 978-0-262-35489-9
    Series Statement: The MIT press essential knowledge series
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-262-53755-1
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Online Resource
    Online Resource
    Cambridge :MIT Press,
    UID:
    edocfu_9961266929102883
    Format: 1 online resource (298 pages).
    Edition: First edition.
    ISBN: 0-262-35490-X , 0-262-35489-6
    Series Statement: MIT Press essential knowledge series
    Content: An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.
    Note: Intro -- Contents -- Series Foreword -- Preface -- Acknowledgments -- 1: Introduction to Deep Learning -- Artificial Intelligence, Machine Learning, and Deep Learning -- What Is Machine Learning? -- Why Is Machine Learning Difficult? -- The Key Ingredients of Machine Learning -- Supervised, Unsupervised, and Reinforcement Learning -- Why Is Deep Learning So Successful? -- Summary and the Road Ahead -- 2: Conceptual Foundations -- What Is a Mathematical Model? -- Linear Models with Multiple Inputs -- Setting the Parameters of a Linear Model -- Learning Model Parameters from Data -- Combining Models -- Input Spaces, Weight Spaces, and Activation Spaces -- Summary -- 3: Neural Networks: The Building Blocks of Deep Learning -- Artificial Neural Networks -- How an Artificial Neuron Processes Information -- Why Is an Activation Function Necessary? -- How Does Changing the Parameters of a Neuron Affect Its Behavior? -- Accelerating Neural Network Training Using GPUs -- Summary -- 4: A Brief History of Deep Learning -- Early Research: Threshold Logic Units -- Connectionism: Multilayer Perceptrons -- The Era of Deep Learning -- Summary -- 5: Convolutional and Recurrent Neural Networks -- Convolutional Neural Networks -- Recurrent Neural Networks -- 6: Learning Functions -- Gradient Descent -- Training a Neural Network Using Backpropagation -- 7: The Future of Deep Learning -- Big Data Driving Algorithmic Innovations -- The Emergence of New Models -- New Forms of Hardware -- The Challenge of Interpretability -- Final Thoughts -- Glossary -- Notes -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- References -- Further Readings -- Books on Deep Learning and Neural Networks -- Online Resources -- Overview Journal Articles -- Index.
    Additional Edition: ISBN 0-262-53755-9
    Language: English
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