Next-Generation Machine Learning with Spark
Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More
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Beschreibung
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.
You will:
- 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 theIsolation 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
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.
For the latest version of Spark and Spark MLlib Covers powerful third-party machine learning algorithms and libraries not available in the standard Spark MLlib library such as XGBoost4J-Spark, LightGBM on Spark, Isolation Forest, Spark NLP, and Stanford CoreNLP Includes distributed deep learning using convolutional neural networks with Spark and Keras
Autor*in
Butch Quinto
Themen in »Next-Generation Machine Learning with Spark«
Spark Big data Machine Learning Spark ML Spark MLlib Spark Machine Learning XGBoost LightGBM NLP Natural Language Processing Stanford CoreNLP Spark NLP Random Forest Logistic Regression Linear Regression