Summary
This chapter has given you some of the Python programming basics required to implement deep learning and neural networks. In the next chapter, we will enter the world of deep learning and look at some actual Python code.
This chapter provided only a brief overview of Python. If you want to learn more, the following materials may be helpful. For Python, Bill Lubanovic: Introducing Python, Second Edition, O'Reilly Media, 2019 is recommended. This is a practical primer that elaborately explains Python programming from its basics to its applications. For NumPy, Wes McKinney: Python for Data Analysis, O'Reilly Media, 2012 is easy to understand and well organized. In addition to these books, the Scipy Lecture Notes (https://scipy-lectures.org) website describes NumPy and Matplotlib in scientific and technological calculations in depth. Refer to them if you are interested.
This chapter covered the following points:
- Python is a programming language that is simple and easy to learn.
- Python is an open-source piece of software that you can use as you like.
- This book uses Python 3 to implement deep learning.
- NumPy and Matplotlib are used as external libraries.
- Python provides two execution modes: interpreter and script files.
- In Python, you can implement and import functions and classes as modules.
- NumPy provides many convenient methods for handling multi-dimensional arrays.
