Bastiaan Sjardin's Large Scale Machine Learning with Python PDF

By Bastiaan Sjardin

Learn to construct strong computing device studying types fast and set up large-scale predictive applications

About This Book

  • Design, engineer and installation scalable laptop studying recommendations with the facility of Python
  • Take command of Hadoop and Spark with Python for potent computing device studying on a map decrease framework
  • Build cutting-edge types and increase custom-made strategies to accomplish laptop studying at scale

Who This publication Is For

This e-book is for an individual who intends to paintings with huge and intricate information units. Familiarity with simple Python and laptop studying options is suggested. operating wisdom in information and computational arithmetic may even be helpful.

What you'll Learn

  • Apply the main scalable laptop studying algorithms
  • Work with glossy cutting-edge large-scale computing device 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 info streams to coach linear and non-linear predictive versions from tremendous huge datasets utilizing a unmarried machine

In Detail

Large Python computer studying initiatives contain new difficulties linked to really expert laptop studying architectures and designs that many information scientists haven't begun to take on. yet discovering algorithms and designing and construction systems that care for huge units of knowledge is a growing to be want. facts scientists need to deal with and continue more and more complicated information tasks, and with the increase of massive facts comes an expanding call for for computational and algorithmic potency. huge Scale laptop studying with Python uncovers a brand new wave of desktop 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 computing device machine with tips about parallelization and reminiscence allocation. familiarize yourself with new algorithms which are in particular designed for giant initiatives and will deal with larger documents, and find out about desktop studying in immense info environments. we'll additionally disguise the best laptop studying thoughts on a map lessen framework in Hadoop and Spark in Python.

Style and Approach

This effective and functional identify is filled jam-packed with the ideas, information and instruments you want to be certain your huge scale Python computer studying runs rapidly and seamlessly.

Large-scale desktop studying tackles a distinct factor to what's at the moment out there. these operating with Hadoop clusters and in info in depth environments can now research powerful methods of creating robust computer studying types from prototype to production.

This e-book is written in a mode that programmers from different languages (R, Julia, Java, Matlab) can follow.

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Extra info for Large Scale Machine Learning with Python

Example text

After installation, you can immediately start using Jupyter, calling it from the command line: $ jupyter notebook Once the Jupyter instance has opened in the browser, click on the New button, and in the Notebooks section, choose Python 2 (other kernels may be present in the section, depending on what you installed): At this point, your new empty notebook will look like the following screenshot and you can start entering the commands in the cells: For instance, you may start typing the following in the cell: In: print ("This is a test") After writing in cells, you just press the play button (below the Cell tab) to run it and obtain an output.

Therefore, with respect to your problem that, so far, you solved by machine learning, you considered, based on experience or empirical tests, that certain algorithms may work better than others did. With large scale problems, you have to add other and different considerations when deciding on the algorithm: How complex your algorithm is; that is, if the number of rows and columns in your data affects the number of computations in a linear or nonlinear way. Should the algorithm learn from all the data at once or can you use single examples or small batches of data instead?

Arrays are blocks of data arranged along multiple dimensions, which implement mathematical vectors and matrices. Arrays are useful not just to store data, but also for fast matrix operations (vectorization), which are indispensable when you wish to solve ad hoc data science problems.

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