Book Image

Mastering Python 2E - Second Edition

By : Rick van Hattem
5 (1)
Book Image

Mastering Python 2E - Second Edition

5 (1)
By: Rick van Hattem

Overview of this book

Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python’s capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code’s performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges
Table of Contents (21 chapters)
19
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20
Index

Summary

This chapter gave you a sample of some of the largest and most popular Python AI libraries, but there are many more (large) libraries around that could be useful for your particular use case. There are, for example, also many libraries available for specific topics such as astronomy, geographical information systems (GISes), protein folding, and neurological imaging.

After this chapter, you should have some idea of where to start searching for particular types of AI libraries. Additionally, you should know a little bit about when to apply a particular type of AI. For many use cases, you will need a combination of these methods to solve the problem in an efficient manner. A supervised ML system, for example, is a fantastic option if you have a vast amount of good-quality, labeled data. Often this is not the case, which is where the other algorithms come in.

Surprisingly enough, many of the current “AI” start-up companies don’t actually use AI for...