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    Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall - okladka książki

    Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall - okladka książki

    Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall - audiobook MP3

    Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall - audiobook CD

    Autorzy:
    Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    666
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.
    This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

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    O autorach książki

    Siamak Amirghodsi (Sammy) is interested in building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP.
    Shuen Mei is a big data analytic platforms expert with 15+ years of experience in designing, building, and executing large-scale, enterprise-distributed financial systems with mission-critical low-latency requirements. He is certified in the Apache Spark, Cloudera Big Data platform, including Developer, Admin, and HBase. He is also a certified AWS solutions architect with emphasis on peta-byte range real-time data platform systems.
    Meenakshi Rajendran is experienced in the end-to-end delivery of data analytics and data science products for leading financial institutions. Meenakshi holds a master's degree in business administration and is a certified PMP with over 13 years of experience in global software delivery environments. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale.
    Broderick Hall is a hands-on big data analytics expert and holds a masters degree in computer science with 20 years of experience in designing and developing complex enterprise-wide software applications with real-time and regulatory requirements at a global scale. He is a deep learning early adopter and is currently working on a large-scale cloud-based data platform with deep learning net augmentation.

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