Deep learning:
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
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, MA ; London, England
The MIT Press
[2019]
|
| Schriftenreihe: | The MIT press essential knowledge series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | 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 "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"-- |
| Beschreibung: | x, 280 Seiten Illustrationen |
| ISBN: | 9780262537551 |
Internformat
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| 520 | 3 | |a "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"-- | |
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Datensatz im Suchindex
| _version_ | 1804180499068878848 |
|---|---|
| adam_text | CONTENTS Series Foreword vii Preface ix Acknowledgments xi 1 2 3 4 5 6 7 Introduction to Deep Learning 1 Conceptual Foundations 39 Neural Networks: The Building Blocks of Deep Learning 65 A Brief History of Deep Learning 101 Convolutional and Recurrent Neural Networks Learning Functions 185 The Future of Deep Learning 231 Glossary 251 Notes 257 References 261 Further Readings Index 269 267 159
|
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| author | Kelleher, John D. 1974- |
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| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
| format | Book |
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| id | DE-604.BV046153971 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:36:44Z |
| institution | BVB |
| isbn | 9780262537551 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031534035 |
| oclc_num | 1122726632 |
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| physical | x, 280 Seiten Illustrationen |
| publishDate | 2019 |
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| publisher | The MIT Press |
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| spelling | Kelleher, John D. 1974- Verfasser (DE-588)1078317925 aut Deep learning John D. Kelleher Cambridge, MA ; London, England The MIT Press [2019] x, 280 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier The MIT press essential knowledge series 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 "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"-- Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Machine learning Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe 978-0-262-35489-9 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf 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 Inhaltsverzeichnis |
| spellingShingle | Kelleher, John D. 1974- Deep learning Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Deep learning |
| title_auth | Deep learning |
| title_exact_search | Deep learning |
| title_full | Deep learning John D. Kelleher |
| title_fullStr | Deep learning John D. Kelleher |
| title_full_unstemmed | Deep learning John D. Kelleher |
| title_short | Deep learning |
| title_sort | deep learning |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | 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 |
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