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  • 1
    Book
    Book
    Data science / (2018)
    Cambridge, Massachusetts ; London, England :The MIT Press,
    Details
    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:
    QH 235
    RVK:
    ST 530
    RVK:
    QP 345
    RVK:
    ST 270
    Keywords: Data Science ; Big Data ; Data Science ; Maschinelles Lernen ; Einführung ; Einführung ; Handbooks and manuals ; Einführung
    URL: Inhaltsverzeichnis
    URL: Cover
    URL: Inhaltsverzeichnis
    URL: Inhaltsverzeichnis
    URL: Inhaltsverzeichnis
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Deep learning / (2019)
    Cambridge, MA ; London, England :The MIT Press,
    Details
    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:
    ST 300
    Keywords: Maschinelles Lernen
    URL: http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031534035&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
    URL: Inhaltsverzeichnis
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Book
    Book
    Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies (2015)
    Cambridge, Massachusetts :MIT Press,
    Details
    UID:
    almafu_BV042758884
    Format: xxii, 595 Seiten : , Diagramme.
    ISBN: 978-0-262-02944-5 , 0-262-02944-8
    Language: English
    Subjects: Computer Science
    RVK:
    ST 302
    RVK:
    ST 601
    RVK:
    ST 300
    Keywords: Maschinelles Lernen ; Datenanalyse
    URL: http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028189454&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
    URL: Inhaltstext
    URL: http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028189454&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
    Library Location Call Number Volume/Issue/Year Availability
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    Stabi Berlin get availability status
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  • 4
    Online Resource
    Online Resource
    Data science / (2018)
    Cambridge, MA :The MIT Press,
    Details
    UID:
    almafu_9961267990402883
    Format: 1 online resource (xiv, 264 pages) : , illustrations.
    ISBN: 0-262-34703-2 , 0-262-34702-4
    Series Statement: The MIT Press essential knowledge series
    Content: 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.--
    Note: Preface -- 1 What is data science? -- 2 What is data and what is a dataset? -- 3 The data science ecosystem -- 4 Machine learning 101 -- 5 Standard data science tasks -- 6 Privacy and ethics -- 7 Future trends and principles of success -- Glossary -- Notes -- Further readings -- References -- Index. , Also available in print.
    Additional Edition: ISBN 0-262-53543-2
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    QH 235
    RVK:
    ST 530
    Keywords: Einführung
    URL: lizenzpflichtig
    URL: Cover
    URL: Inhaltsverzeichnis
    URL: Inhaltsverzeichnis
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    FU Berlin
    Hertie School
  • 5
    Online Resource
    Online Resource
    Deep learning / (2019)
    Cambridge, MA ; London, England :The MIT Press,
    Details
    UID:
    almafu_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:
    ST 300
    Keywords: Maschinelles Lernen
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    FU Berlin
  • 6
    Online Resource
    Online Resource
    Deep learning / (2019)
    Cambridge :MIT Press,
    Details
    UID:
    almafu_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
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    FU Berlin
  • 7
    Online Resource
    Online Resource
    データアナリティクスのための機械学習入門 : アルゴリズム・実例・ケーススタディ (2022)
    近代科学社,
    Details
    UID:
    almafu_9961373503002883
    Format: オンライン資料1件
    Original writing title: Dēta anaritikusu no tame no kikai gakushū nyūmon
    Original writing publisher: Kindaikagakusha,
    ISBN: 4-7649-7290-5
    Series Statement: 世界標準MIT教科書
    Note: 参考文献: p[438]-447
    Additional Edition: ISBN 4-7649-0617-1
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    FU Berlin
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