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Applied Computational Thinking with Python

You're reading from   Applied Computational Thinking with Python Design algorithmic solutions for complex and challenging real-world problems

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781839219436
Length 420 pages
Edition 1st Edition
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Authors (2):
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Dayrene Martinez Dayrene Martinez
Author Profile Icon Dayrene Martinez
Dayrene Martinez
Sofía De Jesús Sofía De Jesús
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Sofía De Jesús
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Computational Thinking
2. Chapter 1: Fundamentals of Computer Science FREE CHAPTER 3. Chapter 2: Elements of Computational Thinking 4. Chapter 3: Understanding Algorithms and Algorithmic Thinking 5. Chapter 4: Understanding Logical Reasoning 6. Chapter 5: Exploring Problem Analysis 7. Chapter 6: Designing Solutions and Solution Processes 8. Chapter 7: Identifying Challenges within Solutions 9. Section 2:Applying Python and Computational Thinking
10. Chapter 8: Introduction to Python 11. Chapter 9: Understanding Input and Output to Design a Solution Algorithm 12. Chapter 10: Control Flow 13. Chapter 11: Using Computational Thinking and Python in Simple Challenges 14. Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
15. Chapter 12: Using Python in Experimental and Data Analysis Problems 16. Chapter 13: Using Classification and Clusters 17. Chapter 14: Using Computational Thinking and Python in Statistical Analysis 18. Chapter 15: Applied Computational Thinking Problems 19. Chapter 16: Advanced Applied Computational Thinking Problems 20. Other Books You May Enjoy

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...

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