Scaling Up Machine Learning Parallel and Distributed Approaches

BOOKS - PROGRAMMING - Scaling Up Machine Learning Parallel and Distributed Approaches

Scaling Up Machine Learning Parallel and Distributed Approaches - Ron Bekkerman, Mikhail Bilenko, John Langford 2011 PDF Cambridge University Press BOOKS PROGRAMMING

1 TON

77293

Scaling Up Machine Learning Parallel and Distributed Approaches

Author: Ron Bekkerman, Mikhail Bilenko, John Langford
Year: 2011
Format: PDF
File size: 10,5 MB
Language: ENG

Pay with Telegram STARS

The book covers the principles, algorithms, and applications of parallel and distributed processing, including map-reduce programming models, parallel database systems, and distributed machine learning. The book provides a comprehensive overview of the challenges and opportunities in scaling up machine learning and data mining methods on parallel and distributed computing platforms. It also discusses the current state of the art in scalable machine learning and data mining techniques, including parallel and distributed algorithms, and their applications in various fields such as computer vision, natural language processing, and bioinformatics. The book concludes by highlighting the future research directions and open challenges in this area. Scaling Up Machine Learning Parallel and Distributed Approaches is a valuable resource for researchers, practitioners, and students who want to learn about the latest developments in scalable machine learning and data mining techniques and their applications in various fields. Book Description: Scaling Up Machine Learning Parallel and Distributed Approaches Authors: [insert author names] Publication Date: [insert publication date] Pages: [insert page count] Publisher: [insert publisher name] ISBN: [insert ISBN number] Summary: This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. It covers the principles, algorithms, and applications of parallel and distributed processing, including map-reduce programming models, parallel database systems, and distributed machine learning.

You may also be interested in:

Scaling Up Machine Learning Parallel and Distributed Approaches

Scaling Python with Dask: From Data Science to Machine Learning

Scaling Python with Dask From Data Science to Machine Learning (Final)

Automating Data Quality Monitoring at Scale Scaling Beyond Rules with Machine Learning (Final)

Scaling Python with Dask From Data Science to Machine Learning (Sixth Early Release)

Simple Machine Learning for Programmers Beginner|s Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applications

Machine Learning for Beginners A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence Algoritms

Python Machine Learning The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science

Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models

Ultimate Parallel and Distributed Computing with Julia For Data Science Excel in Data Analysis, Statistical Modeling and Machine Learning by Leveraging MLBase.jl and MLJ.jl to Optimize Workflows

Machine Learning for Beginners An Introductory Guide to Learn and Understand Artificial Intelligence, Neural Networks and Machine Learning

Building Machine Learning Systems Using Python Practice to Train Predictive Models and Analyze Machine Learning Results

Machine Learning for Business The Ultimate Artificial Intelligence & Machine Learning for Managers, Team Leaders and Entrepreneurs

Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps (English Edition)

Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands

Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices

Machine Learning Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists

Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)

Online Machine Learning: A Practical Guide with Examples in Python (Machine Learning: Foundations, Methodologies, and Applications)

Machine Learning The Ultimate Guide to Understand AI Big Data Analytics and the Machine Learning’s Building Block Application in Modern Life

Machine Learning: A Guide to PyTorch, TensorFlow, and Scikit-Learn: Mastering Machine Learning With Python

Robust Machine Learning: Distributed Methods for Safe AI (Machine Learning: Foundations, Methodologies, and Applications)

Machine Learning for Beginners Build and deploy Machine Learning systems using Python, 2nd Edition

Machine Learning with Core ML 2 and Swift A beginner-friendly guide to integrating machine learning into your apps

Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning

Computer Programming This Book Includes Machine Learning for Beginners, Machine Learning with Python, Deep Learning with Python, Python for Data Analysis

Programming With Python 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow

Machine Learning, Animated (Chapman and Hall CRC Machine Learning and Pattern Recognition)

Pragmatic Machine Learning with Python Learn How to Deploy Machine Learning Models in Production

Machine Learning for Finance Master Financial Strategies with Python-Powered Machine Learning

Machine Learning for Beginners A Practical Guide to Understanding and Applying Machine Learning Concepts

Machine Learning for Absolute Beginners An Absolute beginner’s guide to learning and understanding machine learning successfully