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Mastering Python 2E

You're reading from   Mastering Python 2E Write powerful and efficient code using the full range of Python's capabilities

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Product type Paperback
Published in May 2022
Last Updated in May 2022
Publisher Packt
ISBN-13 9781800207721
Length 710 pages
Edition 2nd Edition
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Author (1):
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Rick Hattem Rick Hattem
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Rick Hattem
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Table of Contents (21) Chapters Close

Preface 1. Getting Started – One Environment per Project FREE CHAPTER 2. Interactive Python Interpreters 3. Pythonic Syntax and Common Pitfalls 4. Pythonic Design Patterns 5. Functional Programming – Readability Versus Brevity 6. Decorators – Enabling Code Reuse by Decorating 7. Generators and Coroutines – Infinity, One Step at a Time 8. Metaclasses – Making Classes (Not Instances) Smarter 9. Documentation – How to Use Sphinx and reStructuredText 10. Testing and Logging – Preparing for Bugs 11. Debugging – Solving the Bugs 12. Performance – Tracking and Reducing Your Memory and CPU Usage 13. asyncio – Multithreading without Threads 14. Multiprocessing – When a Single CPU Core Is Not Enough 15. Scientific Python and Plotting 16. Artificial Intelligence 17. Extensions in C/C++, System Calls, and C/C++ Libraries 18. Packaging – Creating Your Own Libraries or Applications 19. Other Books You May Enjoy
20. Index

Artificial Intelligence

In the last chapter, we saw a collection of scientific Python libraries that allow for really fast and easy processing of large data files. In this chapter, we will use some of these and a few others for machine learning.

Machine learning is a complex subject, and many completely distinct subjects within it are entire branches of research by themselves. This should not discourage you from diving in, however; many of the libraries mentioned in this chapter are really powerful and allow you to get started with a very reasonable amount of effort.

It should be noted that there is a huge difference between applying a pre-trained model and generating your own. Applying a model is usually possible in a few lines of code and barely requires any processing power; building your own model usually takes many lines of code and hours or more to process. This makes the training of models outside of the scope of this book in all but the most trivial cases. In these...

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