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  • 1
    Language: English
    Pages: 1 online resource (1 audio file (18 hr., 30 min.))
    Edition: [First edition].
    DDC: 006.35
    Keywords: Natural language processing (Computer science) ; Python (Computer program language) ; Natural language processing (Computer science) ; Python (Computer program language) ; Downloadable audio books ; Audiobooks ; Audiobooks
    Abstract: "Learn both the theory and practical skills needed to go beyond merely understanding the inner workings of NLP, and start creating your own algorithms or models." Dr. Arwen Griffioen, Zendesk Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries--all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. Inside: Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines This book/course requires a basic understanding of deep learning and intermediate Python skills. Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Provides a great overview of current NLP tools in Python. I'll definitely be keeping this book on hand for my own NLP work. Highly recommended! Tony Mullen, Northeastern University-Seattle An intuitive guide to get you started with NLP. The book is full of programming examples that help you learn in a very pragmatic way. Tommaso Teofili, Adobe Systems NARRATED BY MARK THOMAS
    Note: Online resource; title from title details screen (O'Reilly, viewed May 4, 2022)
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  • 2
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Manning Publications | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (544 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.
    Note: Online resource; Title from title page (viewed April 28, 2019)
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  • 3
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (1 video file, approximately 41 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: TensorFlow Serving is one of the cornerstones in the TensorFlow ecosystem. It has eased the deployment of machine learning models tremendously and led to an acceleration of model deployments. Unfortunately, machine learning engineers aren’t familiar with the details of TensorFlow Serving, and they’re missing out on significant performance increases. Hannes Hapke (SAP ConcurLabs) provides a brief introduction to TensorFlow Serving, then leads a deep dive into advanced settings and use cases. He introduces advanced concepts and implementation suggestions to increase the performance of the TensorFlow Serving setup, which includes an introduction to how clients can request model meta-information from the model server, an overview of model optimization options for optimal prediction throughput, an introduction to batching requests to improve the throughput performance, an example implementation to support model A/B testing, and an overview of monitoring your TensorFlow Serving setup. Prerequisite knowledge A basic understanding of Docker functionality and how HTTP requests work General knowledge of machine learning (useful but not required) What you'll learn Learn how to increase the TensorFlow Serving inference performance, increase the inference response time by tweaking the request payload, and run TensorFlow Serving with NVIDIA's TensorRT for further performance improvements Discover how to configure batch requests in TensorFlow Serving and how to configure TensorFlow Serving to provide A/B Testing capabilities This session is from the 2019 O'Reilly TensorFlow World Conference in Santa Clara, CA.
    Note: Online resource; Title from title screen (viewed February 28, 2020) , Mode of access: World Wide Web.
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  • 4
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Manning Publications | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (1 video file, approximately 17 hr., 26 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: "Learn both the theory and practical skills needed to go beyond merely understanding the inner workings of NLP, and start creating your own algorithms or models." Dr. Arwen Griffioen, Zendesk Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. Inside: Some sentences in this book were written by NLP! Can you guess which ones? Working with Keras, TensorFlow, gensim, and scikit-learn Rule-based and data-based NLP Scalable pipelines This book/course requires a basic understanding of deep learning and intermediate Python skills. Hobson Lane , Cole Howard , and Hannes Max Hapke are experienced NLP engineers who use these techniques in production. Provides a great overview of current NLP tools in Python. I’ll definitely be keeping this book on hand for my own NLP work. Highly recommended! Tony Mullen, Northeastern University–Seattle An intuitive guide to get you started with NLP. The book is full of programming examples that help you learn in a very pragmatic way. Tommaso Teofili, Adobe Systems NARRATED BY MARK THOMAS
    Note: Online resource; Title from title screen (viewed April 29, 2019) , Mode of access: World Wide Web.
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (275 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines with Apache Airflow and Kubeflow Pipelines Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated Design model feedback loops to increase your data sets and learn when to update your machine learning models
    Note: Online resource; Title from title page (viewed August 25, 2020)
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  • 6
    Orig.schr. Ausgabe: 初版.
    Title: 入門機械学習パイプライン : : TensorFlowで学ぶワークフローの自動化 /
    Publisher: オライリー・ジャパン,
    ISBN: 9784873119519 , 4873119510
    Language: Japanese
    Pages: 1 online resource (392 pages)
    Edition: Shohan.
    Uniform Title: Building machine learning pipelines
    DDC: 006.3/1
    Keywords: TensorFlow ; Machine learning ; Cloud computing ; Business enterprises Data processing
    Note: In Japanese.
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  • 7
    Orig.schr. Ausgabe: 第1版.
    Title: 机器学习流水线实战 = : Building machine learning pipelines /
    Publisher: 人民邮电出版社,
    ISBN: 9787115573216 , 7115573212
    Language: Chinese
    Pages: 1 online resource , illustrations.
    Edition: Di 1 ban.
    Series Statement: Tu ling cheng xu she ji cong shu
    Uniform Title: Building machine learning pipelines
    DDC: 006.3/1
    Keywords: TensorFlow ; Machine learning ; Cloud computing ; Business enterprises Data processing ; Apprentissage automatique ; Infonuagique ; Entreprises ; Informatique ; Business enterprises ; Data processing ; Cloud computing ; Machine learning ; Electronic books
    Abstract: Detailed summary in vernacular field.
    Note: Includes bibliographical references
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