Learning TensorFlow: A guide to building deep learning systems - Softcover

Hope, Tom; Resheff, Yehezkel S.; Lieder, Itay

Softcover

ISBN 10: 1491978511 ISBN 13: 9781491978511

Verlag: O'Reilly Media, 2017

Inhaltsangabe

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience-from data scientists and engineers to students and researchers., You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting

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Biografía del autor:

Tom Hope is an applied machine learning researcher and data scientist with extensive background in academia and industry.He has background as a senior data scientist in large international corporation settings, leading data science and deep learning R&D across multiple domains including web mining, text analytics, computer vision, sales and marketing, IoT, financial forecasting and large-scale manufacturing. Previously he was at a successful e-commerce startup in its early days, leading data science R&D. He has also served as a data science consultant for major international companies and startups. His research in computer science, data mining and statistics revolves around machine learning, deep learning, NLP, weak supervision and time-series.Hezi Reshef is an applied researcher and PhD student in Machine Learning at the Hebrew University, developing Machine Learning and Deep Learning methods for wearable device data, and working on using wearable devices to monitor patient health. He has worked at Intel Corp., leading Deep Learning R&D for monitoring and predicting patient outcomes using remote sensing and wearables. Prior to Intel, Hezi was at Microsoft, leading Machine Learning R&D for mining telemetry data, predicting software bugs, user segmentation, and other projects.Itay Lieder is an applied researcher in Machine Learning and Computational Neuroscience and a PhD student at the Hebrew University, in collaboration with the Gatsby Computational Neuroscience Unit at UCL, studying the human perception with massive crowd-sourcing experiments on Amazon Turk. His current work focuses on predicting and understanding the way humans react to sounds (e.g. music), via multiple online interactive experiments. He has worked for large international corporations, leading Deep Learning R&D in text analytics and web mining for sales and marketing.

Biografía del autor:

Tom Hope is an applied researcher in Computer Science, and a PhD student at the Hebrew University, working on Machine Learning, Deep Learning, Natural Language Processing (NLP) and network data. He has worked for Intel Corp. for the past 4 years, leading data science and Deep Learning R&D across multiple projects and domains including in web mining, text analytics, sales and marketing, IoT (machine-generated data), financial forecasting, large-scale manufacturing. He is co-founder of two stealth start-ups in analytics / Deep Learning. Previously he was at Tapingo (an e-commerce start-up) in its early stages, doing end-to-end data science R&D in cold-start conditions. In the past, Tom dabbled in journalism, writing for an English-language international newspaper.

Hezi Reshef is an applied researcher and PhD student in Machine Learning at the Hebrew University, developing Machine Learning and Deep Learning methods for wearable device data, and working on using wearable devices to monitor patient health. He has worked at Intel Corp., leading Deep Learning R&D for monitoring and predicting patient outcomes using remote sensing and wearables. Prior to Intel, Hezi was at Microsoft, leading Machine Learning R&D for mining telemetry data, predicting software bugs, user segmentation, and other projects. Hezi is a co-founder of a start-up focusing on Deep Learning.

Itay Lieder is an applied researcher in Machine Learning and Computational Neuroscience and a PhD student at the Hebrew University in collaboration with the Gatsby Computational Neuroscience Unit at UCL, studying the human perception with massive crowd-sourcing experiments on Amazon Turk. His current work focuses on predicting and understanding the way humans react to sounds (e.g. music), via multiple online interactive experiments. He has worked for Intel Corp., leading Deep Learning R&D in sales and marketing. Itay is a co-founder of a start-up doing analytics with Deep Learning, and a lecturer teaching engineering students.

Über diese Auflage

• Verlag: O'Reilly Media
• Erscheinungsdatum: 2017
• ISBN 10: 1491978511
• ISBN 13: 9781491978511
• Einband: Tapa blanda
• Anzahl der Seiten: 242