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Learning Ray Max Pumperla, Edward Oakes, Richard Liaw

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Learning Ray Max Pumperla, Edward Oakes, Richard Liaw - okladka książki

Learning Ray Max Pumperla, Edward Oakes, Richard Liaw - okladka książki

Learning Ray Max Pumperla, Edward Oakes, Richard Liaw - audiobook MP3

Learning Ray Max Pumperla, Edward Oakes, Richard Liaw - audiobook CD

Autorzy:
Max Pumperla, Edward Oakes, Richard Liaw
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
274
Dostępne formaty:
     ePub
     Mobi

Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.

Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.

  • Learn how to build your first distributed applications with Ray Core
  • Conduct hyperparameter optimization with Ray Tune
  • Use the Ray RLlib library for reinforcement learning
  • Manage distributed training with the Ray Train library
  • Use Ray to perform data processing with Ray Datasets
  • Learn how work with Ray Clusters and serve models with Ray Serve
  • Build end-to-end machine learning applications with Ray AIR

Wybrane bestsellery

O autorze książki

Max Pumperla is a data scientist and engineer specializing in deep learning and its applications. He currently works as a deep learning engineer at Skymind and is a co-founder of aetros.com. Max is the author and maintainer of several Python packages, including elephas, a distributed deep learning library using Spark. His open source footprint includes contributions to many popular machine learning libraries, such as keras, deeplearning4j, and hyperopt. He holds a PhD in algebraic geometry from the University of Hamburg.

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