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Bastiaan Sjardin, Luca Massaron, Alberto Boschetti - Large Scale Machine Learning with Python [2016, PDF/EPUB, ENG] +Code

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Post 15-Sep-2016 01:01

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Large Scale Machine Learning with Python
Год издания: 2016
Автор: Bastiaan Sjardin, Luca Massaron, Alberto Boschetti
Жанр или тематика: Программирование
Издательство: Packt Publishing
ISBN: 9781785887215
Язык: Английский
Формат: PDF/EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 420
Описание: Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.
Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
What You Will Learn
- Apply the most scalable machine learning algorithms
- Work with modern state-of-the-art large-scale machine learning techniques
- Increase predictive accuracy with deep learning and scalable data-handling techniques
- Improve your work by combining the MapReduce framework with Spark
- Build powerful ensembles at scale
- Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine

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Table of Contents
1: First Steps to Scalability
2: Scalable Learning in Scikit-learn
3: Fast SVM Implementations
4: Neural Networks and Deep Learning
5: Deep Learning with TensorFlow
6: Classification and Regression Trees at Scale
7: Unsupervised Learning at Scale
8: Distributed Environments – Hadoop and Spark
9: Practical Machine Learning with Spark
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