Scaling Up Machine Learning

This book PDF is perfect for those who love Computers genre, written by Ron Bekkerman and published by Cambridge University Press which was released on 28 March 2024 with total hardcover pages 493. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Scaling Up Machine Learning books below.

Scaling Up Machine Learning
Author : Ron Bekkerman
File Size : 47,6 Mb
Publisher : Cambridge University Press
Language : English
Release Date : 28 March 2024
ISBN : 9780521192248
Pages : 493 pages
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Scaling Up Machine Learning by Ron Bekkerman Book PDF Summary

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Scaling Up Machine Learning

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

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Human in the Loop Machine Learning

Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices

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Machine Learning Models and Algorithms for Big Data Classification

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book

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Large Scale Machine Learning with Python

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and

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Distributed Machine Learning Patterns

Practical patterns for scaling machine learning from your laptop to a distributed cluster. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges

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Machine Learning with Python Cookbook

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical

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Data Intensive Text Processing with MapReduce

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever.

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Machine Learning Systems

Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes

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