Machine Learning with Spark
- Išparduota
- A practical tutorial with real-world use cases allowing you to develop your own machine learning systems with Spark- Combine various techniques and models into an intelligent machine learning system- Use Spark's powerful tools to load, analyze, clean, and transform your dataApache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combin…
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- Autorius: Nick Pentreath
- Leidėjas: Packt Publishing
- Metai: 2015
- Puslapiai: 338
- ISBN-10: 1783288515
- ISBN-13: 9781783288519
- Formatas: 18.9 x 23.6 x 2.2 cm, minkšti viršeliai
- Kalba: Anglų
Atsiliepimai
Aprašymas
- A practical tutorial with real-world use cases allowing you to develop your own machine learning systems with Spark- Combine various techniques and models into an intelligent machine learning system
- Use Spark's powerful tools to load, analyze, clean, and transform your data
Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, and powerful API design.
This book guides you through the basics of Spark's API used to load and process data and prepare the data to use as input to the various machine learning models. There are detailed examples and real-world use cases for you to explore common machine learning models including recommender systems, classification, regression, clustering, and dimensionality reduction. You will cover advanced topics such as working with large-scale text data, and methods for online machine learning and model evaluation using Spark Streaming.
Išparduota
- Autorius: Nick Pentreath
- Leidėjas: Packt Publishing
- Metai: 2015
- Puslapiai: 338
- ISBN-10: 1783288515
- ISBN-13: 9781783288519
- Formatas: 18.9 x 23.6 x 2.2 cm, minkšti viršeliai
- Kalba: Anglų
- A practical tutorial with real-world use cases allowing you to develop your own machine learning systems with Spark
- Combine various techniques and models into an intelligent machine learning system
- Use Spark's powerful tools to load, analyze, clean, and transform your data
Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, and powerful API design.
This book guides you through the basics of Spark's API used to load and process data and prepare the data to use as input to the various machine learning models. There are detailed examples and real-world use cases for you to explore common machine learning models including recommender systems, classification, regression, clustering, and dimensionality reduction. You will cover advanced topics such as working with large-scale text data, and methods for online machine learning and model evaluation using Spark Streaming.
- Combine various techniques and models into an intelligent machine learning system
- Use Spark's powerful tools to load, analyze, clean, and transform your data
Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, and powerful API design.
This book guides you through the basics of Spark's API used to load and process data and prepare the data to use as input to the various machine learning models. There are detailed examples and real-world use cases for you to explore common machine learning models including recommender systems, classification, regression, clustering, and dimensionality reduction. You will cover advanced topics such as working with large-scale text data, and methods for online machine learning and model evaluation using Spark Streaming.
Atsiliepimai