Book Image

Applied Computational Thinking with Python

By : Sofía De Jesús, Dayrene Martinez
Book Image

Applied Computational Thinking with Python

By: Sofía De Jesús, Dayrene Martinez

Overview of this book

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
Table of Contents (21 chapters)
Section 1: Introduction to Computational Thinking
Section 2:Applying Python and Computational Thinking
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
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Problem 8 – Using Python to create a convolutional neural network (CNN)

In this section, we're going to take a look at a problem that uses artificial intelligence (AI). More specifically, we're going to work on creating a convolutional neural network, or CNN. So what is a CNN? A CNN is a Deep Learning algorithm. CNNs take images as input. The image is then processed and given importance based on predetermined conditions that will help us differentiate and classify the images.

The following diagram illustrates the process involved in the convolutional neural network:

Figure 16.24 – Convolutional neural network process

The CNN is created in order to simplify how we categorize the images without sacrificing accuracy in terms of the predictions we want to be able to get from our image analyses. It's like if we were applying a filter. Once we apply the filter, we can see the characteristics. The preceding diagram shows a simplified...