Book Image

The Python Workshop - Second Edition

By : Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee
4.7 (3)
Book Image

The Python Workshop - Second Edition

4.7 (3)
By: Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee

Overview of this book

Python is among the most popular programming languages in the world. It’s ideal for beginners because it’s easy to read and write, and for developers, because it’s widely available with a strong support community, extensive documentation, and phenomenal libraries – both built-in and user-contributed. This project-based course has been designed by a team of expert authors to get you up and running with Python. You’ll work though engaging projects that’ll enable you to leverage your newfound Python skills efficiently in technical jobs, personal projects, and job interviews. The book will help you gain an edge in data science, web development, and software development, preparing you to tackle real-world challenges in Python and pursue advanced topics on your own. Throughout the chapters, each component has been explicitly designed to engage and stimulate different parts of the brain so that you can retain and apply what you learn in the practical context with maximum impact. By completing the course from start to finish, you’ll walk away feeling capable of tackling any real-world Python development problem.
Table of Contents (16 chapters)
13
Chapter 13: The Evolution of Python – Discovering New Python Features

Introduction to deep learning

The neurons in a human brain are analogously referred to as nodes in deep learning algorithms. Individual nodes may be thought of as computational units. Although they may stand alone, they are more powerful when connected to one another.

As a visual, here is the Boston Housing DataFrame from Chapter 11, Machine Learning. Each column in the following DataFrame can be represented as a node, as can each entry:

Figure 12.1 – Sample from the Boston Housing dataset

In linear regression, using the standard machine learning algorithm introduced in Chapter 11, Machine Learning, each column, or node, is multiplied by a constant, called a weight, and the individual weights are summed together to make a prediction, as in the following diagram:

Figure 12.2 – Linear regression diagram from Berkeley Coding Academy, created by the author Corey Wade

The process is linear because it uses the simple technique...