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Mastering Python 2E

Mastering Python 2E - Second Edition

By : Rick Hattem
4.6 (65)
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Mastering Python 2E

Mastering Python 2E

4.6 (65)
By: Rick 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)
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19
Other Books You May Enjoy
20
Index

Exercises

Due to the nature of this chapter, all topics only cover the absolute basics of the mentioned libraries and they really do deserve much more. In this case, as an exercise, I recommend that you try and use some (or all) of the mentioned libraries and see if you can do something useful with them.

Some suggestions:

  • Browse through TensorFlow Hub and apply some models to your own data. Perhaps you can apply object detection to your holiday photos.
  • After applying a model to your photos, try and improve the model by adding some new objects and finetuning it.
  • Try to extract some data or information from this chapter’s summary by applying one of the NLP algorithms.

AI is a complicated subject, and even simple example guides are often quite elaborate. Luckily, these days we can often immediately try examples online through Google Colab or by running a Jupyter Notebook. Dive in and don’t get discouraged; there is an incredible amount...

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Mastering Python 2E
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