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Large Scale Machine Learning with Python
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Large Scale Machine Learning with Python
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Overview of this book
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.
Table of Contents (12 chapters)
Preface
Free Chapter
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
A. Introduction to GPUs and Theano
Index
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