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  • Birmingham, UK : Packt Publishing  (6)
  • Hoboken : Taylor and Francis
  • London [u.a.] : Routledge
  • Machine learning  (6)
  • Computer Science  (6)
  • 1
    ISBN: 9781788398879 , 1788398874
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    Keywords: Google (Firm) ; Machine learning ; Cloud computing
    Note: Description based on online resource; title from title page (Safari, viewed May 30, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 2
    ISBN: 9781788834735 , 1788834739
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    Keywords: ANACONDA (Electronic resource) ; Machine learning ; Information visualization ; Electronic data processing
    Note: Description based on online resource; title from title page (Safari, viewed June 27, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    ISBN: 9781788477758 , 1788477758
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    RVK:
    Keywords: Natural language processing (Computer science) ; Machine learning ; Python (Computer program language) ; TensorFlow ; Sprachverarbeitung ; Sprachverarbeitung ; TensorFlow
    Note: Description based on online resource; title from title page (Safari, viewed June 27, 2018). - Includes index
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 4
    ISBN: 9781788291866 , 1788291867
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    Keywords: Machine learning ; Artificial intelligence ; Python (Computer program language)
    Note: Description based on online resource; title from title page (Safari, viewed January 23, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 5
    ISBN: 9781783555147 , 1783555149
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    DDC: 005.13/3
    RVK:
    Keywords: Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Pylearn 2 and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer som...
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed October 12, 2015)
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  • 6
    ISBN: 9781783558759 , 178355875X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    DDC: 005.13
    RVK:
    Keywords: Scala (Computer program language) ; Machine learning ; Regression analysis ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Leverage Scala and Machine Learning to construct and study systems that can learn from data In Detail The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering designs, biometrics, and trading strategies, to detection of genetic anomalies. The book begins with an introduction to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. Next, you'll learn about data preprocessing and filtering techniques. Following this, you'll move on to clustering and dimension reduction, Naïve Bayes, regression models, sequential data, regularization and kernelization, support vector machines, neural networks, generic algorithms, and re-enforcement learning. A review of the Akka framework and Apache Spark clusters concludes the tutorial. What You Will Learn Build dynamic workflows for scientific computing Leverage open source libraries to extract patterns from time series Write your own classification, clustering, or evolutionary algorithm Perform relative performance tuning and evaluation of Spark Master probabilistic models for sequential data Experiment with advanced techniques such as regularization and kernelization Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to a technical analysis of financial markets
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed January 22, 2015)
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