Designing deep learning systems: a guide for software engineers
Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models. In Engineering Deep Learning Systems you will learn how to: Transfer your software development skills to deep learnin...
Gespeichert in:
| Beteiligte Personen: | , |
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| Weitere beteiligte Personen: | , , |
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Shelter Island
Manning
[2023]
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| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034973062&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Zusammenfassung: | Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models. In Engineering Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systemsRecognize and solve common engineering challenges for deep learning systemsUnderstand the deep learning development cycleAutomate training for models in TensorFlow and PyTorchOptimize dataset management, training, model serving and hyperparameter tuningPick the right open-source project for your platformEngineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning. It s full of hands-on examples that will help you transfer your software development skills to implementing deep learning platforms. You ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting-and lucrative-career as a deep learning engineer. about the technology Behind every deep learning researcher is a team of engineers bringing their models to production. To build these systems, you need to understand how a deep learning system s platform differs from other distributed systems. By mastering the core ideas in this book, you ll be able to support deep learning systems in a way that s fast, repeatable, and reliable |
| Umfang: | xx, 337 Seiten Illustrationen, Diagramme |
| ISBN: | 9781633439863 |
Internformat
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| 520 | |a Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models. In Engineering Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systemsRecognize and solve common engineering challenges for deep learning systemsUnderstand the deep learning development cycleAutomate training for models in TensorFlow and PyTorchOptimize dataset management, training, model serving and hyperparameter tuningPick the right open-source project for your platformEngineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning. It s full of hands-on examples that will help you transfer your software development skills to implementing deep learning platforms. You ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting-and lucrative-career as a deep learning engineer. about the technology Behind every deep learning researcher is a team of engineers bringing their models to production. To build these systems, you need to understand how a deep learning system s platform differs from other distributed systems. By mastering the core ideas in this book, you ll be able to support deep learning systems in a way that s fast, repeatable, and reliable | ||
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Datensatz im Suchindex
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| author | Wang, Chi Szeto, Donald |
| author2 | Xue, Yan Savarese, Silvio Xiong, Caiming |
| author2_role | ctb ctb ctb |
| author2_variant | y x yx s s ss c x cx |
| author_GND | (DE-588)1135198101 |
| author_facet | Wang, Chi Szeto, Donald Xue, Yan Savarese, Silvio Xiong, Caiming |
| author_role | aut aut |
| author_sort | Wang, Chi |
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| bvnumber | BV049629199 |
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| ctrlnum | (OCoLC)1437841246 (DE-599)KXP1852844388 |
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| discipline | Informatik |
| format | Book |
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| id | DE-604.BV049629199 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T20:17:14Z |
| institution | BVB |
| isbn | 9781633439863 |
| language | English |
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| oclc_num | 1437841246 |
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| physical | xx, 337 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Manning |
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| spelling | Wang, Chi Verfasser aut Designing deep learning systems a guide for software engineers Chi Wang and Donald Szeto ; code lab by Yan Xue ; foreword by Silvio Savarese and Caiming Xiong Shelter Island Manning [2023] xx, 337 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models. In Engineering Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systemsRecognize and solve common engineering challenges for deep learning systemsUnderstand the deep learning development cycleAutomate training for models in TensorFlow and PyTorchOptimize dataset management, training, model serving and hyperparameter tuningPick the right open-source project for your platformEngineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning. It s full of hands-on examples that will help you transfer your software development skills to implementing deep learning platforms. You ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting-and lucrative-career as a deep learning engineer. about the technology Behind every deep learning researcher is a team of engineers bringing their models to production. To build these systems, you need to understand how a deep learning system s platform differs from other distributed systems. By mastering the core ideas in this book, you ll be able to support deep learning systems in a way that s fast, repeatable, and reliable Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Software Engineering (DE-588)4116521-4 gnd rswk-swf COM060180 Maschinelles Lernen Software Engineering Web services Webservices Maschinelles Lernen (DE-588)4193754-5 s Software Engineering (DE-588)4116521-4 s DE-604 Szeto, Donald Verfasser aut Xue, Yan ctb Savarese, Silvio (DE-588)1135198101 ctb Xiong, Caiming ctb Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034973062&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wang, Chi Szeto, Donald Designing deep learning systems a guide for software engineers Maschinelles Lernen (DE-588)4193754-5 gnd Software Engineering (DE-588)4116521-4 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4116521-4 |
| title | Designing deep learning systems a guide for software engineers |
| title_auth | Designing deep learning systems a guide for software engineers |
| title_exact_search | Designing deep learning systems a guide for software engineers |
| title_full | Designing deep learning systems a guide for software engineers Chi Wang and Donald Szeto ; code lab by Yan Xue ; foreword by Silvio Savarese and Caiming Xiong |
| title_fullStr | Designing deep learning systems a guide for software engineers Chi Wang and Donald Szeto ; code lab by Yan Xue ; foreword by Silvio Savarese and Caiming Xiong |
| title_full_unstemmed | Designing deep learning systems a guide for software engineers Chi Wang and Donald Szeto ; code lab by Yan Xue ; foreword by Silvio Savarese and Caiming Xiong |
| title_short | Designing deep learning systems |
| title_sort | designing deep learning systems a guide for software engineers |
| title_sub | a guide for software engineers |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Software Engineering (DE-588)4116521-4 gnd |
| topic_facet | Maschinelles Lernen Software Engineering |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034973062&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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