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Paperback. Zustand: New. Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorialAbout This Book. Process and analyze big data in a distributed and scalable way. Write sophisticated Spark pipelines that incorporate elaborate extraction. Build and use regression models to predict flight delays Who This Book Is ForAre you a developer with a background in machine learning and statistics who is feeling limited by the current slow and "small data" machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.What You Will Learn. Use Spark streams to cluster tweets online. Run the PageRank algorithm to compute user influence. Perform complex manipulation of DataFrames using Spark. Define Spark pipelines to compose individual data transformations. Utilize generated models for off-line/on-line prediction. Transfer the learning from an ensemble to a simpler Neural Network. Understand basic graph properties and important graph operations. Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language. Use K-means algorithm to cluster movie reviews datasetIn DetailThe purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter.This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification.Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.Style and approachThis book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of mach.…

Paperback. Zustand: New. Run efficient deep learning models on Apache Spark using TensorFlow and KerasKey FeaturesTrain distributed complex neural networks on Apache SparkUse TensorFlow and Keras to train and deploy deep learning modelsExplore practical tips to enhance performanceBook DescriptionOrganizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they're looking to gain faster and more powerful insights from their data. With this book, you'll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You'll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.What you will learnSet up a fully functional Spark environmentUnderstand practical machine learning and deep learning conceptsEmploy built-in machine learning libraries within SparkDiscover libraries that are compatible with TensorFlow and KerasExplore NLP models such as word2vec and TF-IDF on SparkOrganize DataFrames for deep learning evaluationApply testing and training modeling to ensure accuracyAccess readily available code that can be reusedWho this book is forIf you're looking for a practical resource for implementing efficiently distributed deep learning models with Apache Spark, then this book is for you. Knowledge of core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the most out of this book. Some knowledge of Python programming will also be useful.…

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Paperback. Zustand: New. Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorialAbout This Book. Process and analyze big data in a distributed and scalable way. Write sophisticated Spark pipelines that incorporate elaborate extraction. Build and use regression models to predict flight delays Who This Book Is ForAre you a developer with a background in machine learning and statistics who is feeling limited by the current slow and "small data" machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.What You Will Learn. Use Spark streams to cluster tweets online. Run the PageRank algorithm to compute user influence. Perform complex manipulation of DataFrames using Spark. Define Spark pipelines to compose individual data transformations. Utilize generated models for off-line/on-line prediction. Transfer the learning from an ensemble to a simpler Neural Network. Understand basic graph properties and important graph operations. Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language. Use K-means algorithm to cluster movie reviews datasetIn DetailThe purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter.This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification.Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.Style and approachThis book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of mach.…

Paperback. Zustand: New. Run efficient deep learning models on Apache Spark using TensorFlow and KerasKey FeaturesTrain distributed complex neural networks on Apache SparkUse TensorFlow and Keras to train and deploy deep learning modelsExplore practical tips to enhance performanceBook DescriptionOrganizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they're looking to gain faster and more powerful insights from their data. With this book, you'll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You'll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.What you will learnSet up a fully functional Spark environmentUnderstand practical machine learning and deep learning conceptsEmploy built-in machine learning libraries within SparkDiscover libraries that are compatible with TensorFlow and KerasExplore NLP models such as word2vec and TF-IDF on SparkOrganize DataFrames for deep learning evaluationApply testing and training modeling to ensure accuracyAccess readily available code that can be reusedWho this book is forIf you're looking for a practical resource for implementing efficiently distributed deep learning models with Apache Spark, then this book is for you. Knowledge of core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the most out of this book. Some knowledge of Python programming will also be useful.…

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