Next-Generation Machine Learning with Spark (eBook)
XIX, 355 Seiten
Apress (Verlag)
978-1-4842-5669-5 (ISBN)
Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications.
Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations.
What You Will Learn
- Be introduced to machine learning, Spark, and Spark MLlib 2.4.x
- Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries
- Detect anomalies with the Isolation Forest algorithm for Spark
- Use the Spark NLP and Stanford CoreNLP libraries that support multiple languages
- Optimize your ML workload with the Alluxio in-memory data accelerator for Spark
- Use GraphX and GraphFrames for Graph Analysis
- Perform image recognition using convolutional neural networks
- Utilize the Keras framework and distributed deep learning libraries with Spark
Who This Book Is For
Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.
Butch Quinto is founder and Chief AI Officer at Intelvi AI, an artificial intelligence company that develops cutting-edge solutions for the defense, industrial, and transportation industries. As Chief AI Officer, Butch heads strategy, innovation, research, and development. Previously, he was the Director of Artificial Intelligence at a leading technology firm and Chief Data Officer at an AI startup. As Director of Analytics at Deloitte, Butch led the development of several enterprise-grade AI and IoT solutions as well as strategy, business development, and venture capital due diligence. He has more than 20 years of experience in various technology and leadership roles in several industries including banking and finance, telecommunications, government, utilities, transportation, e-commerce, retail, manufacturing, and bioinformatics. Butch is the author of Next-Generation Big Data (Apress) and a member of the Association for the Advancement of Artificial Intelligence and the American Association for the Advancement of Science.
Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications.The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry.Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. What You Will LearnBe introduced to machine learning, Spark, and Spark MLlib 2.4.xAchieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM librariesDetect anomalies with the Isolation Forest algorithm for SparkUse the Spark NLP and Stanford CoreNLP libraries that support multiple languagesOptimize your ML workload with the Alluxio in-memory data accelerator for SparkUse GraphX and GraphFrames for Graph AnalysisPerform image recognition using convolutional neural networksUtilize the Keras framework and distributed deep learning libraries with Spark Who This Book Is ForData scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.
| Erscheint lt. Verlag | 22.2.2020 |
|---|---|
| Zusatzinfo | XIX, 355 p. 67 illus. |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Schlagworte | Big Data • Distributed Computing • LightGBM • linear regression • Logistic Regression • machine learning • Natural Language Processing • NLP • random forest • Spark • Spark Machine Learning • Spark ML • Spark MLlib • Spark NLP • Stanford CoreNLP • xgboost |
| ISBN-10 | 1-4842-5669-7 / 1484256697 |
| ISBN-13 | 978-1-4842-5669-5 / 9781484256695 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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