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Machine Learning with R

You're reading from   Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801071321
Length 762 pages
Edition 4th Edition
Languages
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Concepts
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (19) Chapters Close

Preface 1. Thinking Computationally 2. Abstraction in Detail FREE CHAPTER 3. Algorithmic Thinking and Complexity 4. Understanding the Machine 5. Data Structures 6. Reusing Your Code and Modularity 7. Outlining the Challenge 8. Building a Simple Command-Line Interface 9. Reading Data from Different Formats 10. Finding Information in Text 11. Clustering Data 12. Reflecting on What We Have Built 13. The Problems of Scale 14. Dealing with GPUs and Specialized Hardware 15. Profiling Your Code 16. Unlock Your Exclusive Benefits 17. Other Books You May Enjoy 18. Index

Separating components in the build system

Once a project reaches a certain size, it becomes rather difficult to manage as a single large project. One strategy for dealing with this is to separate the large project into small components, at least within the build system. However, this carries some risks, and it can be rather difficult to get right, particularly if the output of the large project is a shared library. One instance where these issues appear is in testing. Testing the internals of a shared library can be rather problematic, since these details might not be externally visible (exported). Sometimes these internals don’t need to be tested in isolation, but if you do, pulling these into a small static library that is linked into the larger library and tested independently is one strategy. Understanding when and how one can separate parts into their own component and how to integrate them into the larger whole is crucial to getting this right.

The key to modularizing...

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