By Bastiaan Sjardin
Learn to construct strong laptop studying versions quick and set up large-scale predictive applications
About This Book
- Design, engineer and installation scalable desktop studying options with the facility of Python
- Take command of Hadoop and Spark with Python for potent desktop studying on a map lessen framework
- Build cutting-edge versions and advance custom-made thoughts to accomplish computer studying at scale
Who This ebook Is For
This e-book is for somebody who intends to paintings with huge and complicated facts units. Familiarity with easy Python and laptop studying options is suggested. operating wisdom in facts and computational arithmetic may even be helpful.
What you'll Learn
- Apply the main scalable computer studying algorithms
- Work with sleek state of the art large-scale laptop studying techniques
- Increase predictive accuracy with deep studying and scalable data-handling techniques
- Improve your paintings by way of combining the MapReduce framework with Spark
- Build strong ensembles at scale
- Use information streams to coach linear and non-linear predictive types from super huge datasets utilizing a unmarried machine
Large Python desktop studying initiatives contain new difficulties linked to really expert desktop studying architectures and designs that many information scientists have not begun to take on. yet discovering algorithms and designing and development systems that care for huge units of information is a growing to be desire. facts scientists need to deal with and retain more and more advanced information initiatives, and with the increase of huge information comes an expanding call for for computational and algorithmic potency. huge Scale desktop studying with Python uncovers a brand new wave of laptop studying algorithms that meet scalability calls for including a excessive predictive accuracy.
Dive into scalable laptop studying and the 3 kinds of scalability. accelerate algorithms that may be used on a computer desktop with tips about parallelization and reminiscence allocation. become familiar with new algorithms which are particularly designed for big tasks and will deal with greater documents, and know about desktop studying in great facts environments. we'll additionally hide the best computer studying options on a map decrease framework in Hadoop and Spark in Python.
Style and Approach
This effective and sensible identify is filled filled with the suggestions, counsel and instruments you want to confirm your huge scale Python computer studying runs quickly and seamlessly.
Large-scale laptop studying tackles a distinct factor to what's presently out there. these operating with Hadoop clusters and in information in depth environments can now research potent methods of creating robust computer studying types from prototype to production.
This booklet is written in a method that programmers from different languages (R, Julia, Java, Matlab) can follow.
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Additional info for Large Scale Machine Learning with Python
The memory garbage collector will often save the day when you load, transform, dice, slice, save, or discard data using the various iterations and reiterations of data wrangling. org/. Scale up with Python Python is an interpreted language; it runs the reading of your script from memory and executes it during runtime, thus accessing the necessary resources (files, objects in memory, and so on). Apart from being interpreted, another important aspect to take into consideration when using Python for data analysis and machine learning is that Python is single-threaded.
CART, an acronym for classification and regression trees, is a machine learning method usually applied in the framework of ensemble methods. We will also provide examples of a large-scale application using H2O. Chapter 7, Unsupervised Learning at Scale, dives into unsupervised learning, as we will cover PCA, cluster analysis, and topic modeling using the right approach for scaling them up. Chapter 8, Distributed Environments – Hadoop and Spark, teaches us how to set up Spark within a virtual machine environment, shifting from a single machine to a computational network paradigm.
This occurs either in the form of labels and classes (classification problems) or in the form of a continuous value (regression problems). Tangible examples of machine learning in real-life applications range from predicting future stock prices to classifying the gender of an author from a set of documents. Throughout this book, the most important machine learning concepts, together with methods suitable for larger datasets, will be made clear to the reader, thanks to practical examples in Python.