Name: Scaling Machine Learning With Spark: Distributed ml With Mllib, Tensorflow, and Pytorch (en Inglés)
Price: 70820.00 CLP
Availability: InStock
Author: Polak, Adi
ISBN: 9781098106829

portada Scaling Machine Learning With Spark: Distributed ml With Mllib, Tensorflow, and Pytorch (en Inglés)

Scaling Machine Learning With Spark: Distributed ml With Mllib, Tensorflow, and Pytorch (en Inglés)

Formato

Libro Físico

Autor

Adi Polak

Editorial

O'Reilly Media

Idioma

Inglés

N° páginas

300

Encuadernación

Tapa Blanda

ISBN13

9781098106829

N° edición

Categorías

Programación informática/desarrollo de software
Aprendizaje automático («machine learning»)

Scaling Machine Learning With Spark: Distributed ml With Mllib, Tensorflow, and Pytorch (en Inglés)

Adi Polak (Autor) · O'Reilly Media · Tapa Blanda

Libro Nuevo Importado

Envío: 16 a 21 días háb.

$ 141.640$ 70.820

-50%

Libro UsadoBuen Estado Origen: Chile

Envío Rápido

$ 20.940

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 100 unidades

$ 70.820

Llega entre el 14 Ago y el 21 Ago a Santiago, Región Metropolitana. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Scaling Machine Learning With Spark: Distributed ml With Mllib, Tensorflow, and Pytorch (en Inglés)"

Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you're looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you. Using Spark as your main data processing platform, you'll discover several open source technologies designed and built for enriching Spark's ML capabilities. Scaling Machine Learning with Spark examines various technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLFlow, TensorFlow, PyTorch, and Petastorm. This book shows you when to use each technology and why. If you're a data scientist working with machine learning, you'll learn how to: Build practical distributed machine learning workflows, including feature engineering and data formats Extend deep learning functionalities beyond Spark by bridging into distributed TensorFlow and PyTorch Manage your machine learning experiment lifecycle with MLFlow Use Petastorm as a storage layer for bridging data from Spark into TensorFlow and PyTorch Use machine learning terminology to understand distribution strategies