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The Python Workshop Second Edition - Second Edition

You're reading from  The Python Workshop Second Edition - Second Edition

Product type Book
Published in Nov 2022
Publisher Packt
ISBN-13 9781804610619
Pages 600 pages
Edition 2nd Edition
Languages
Authors (5):
Corey Wade Corey Wade
Profile icon Corey Wade
Mario Corchero Jiménez Mario Corchero Jiménez
Profile icon Mario Corchero Jiménez
Andrew Bird Andrew Bird
Profile icon Andrew Bird
Dr. Lau Cher Han Dr. Lau Cher Han
Profile icon Dr. Lau Cher Han
Graham Lee Graham Lee
Profile icon Graham Lee
View More author details

Table of Contents (16) Chapters

Preface 1. Chapter 1: Python Fundamentals – Math, Strings, Conditionals, and Loops 2. Chapter 2: Python Data Structures 3. Chapter 3: Executing Python – Programs, Algorithms, and Functions 4. Chapter 4: Extending Python, Files, Errors, and Graphs 5. Chapter 5: Constructing Python – Classes and Methods 6. Chapter 6: The Standard Library 7. Chapter 7: Becoming Pythonic 8. Chapter 8: Software Development 9. Chapter 9: Practical Python – Advanced Topics 10. Chapter 10: Data Analytics with pandas and NumPy 11. Chapter 11: Machine Learning 12. Chapter 12: Deep Learning with Python 13. Chapter 13: The Evolution of Python – Discovering New Python Features 14. Index 15. Other Books You May Enjoy

Convolutional neural networks

Although deep learning performs well on tabular regression and classification datasets, deep learning has a bigger advantage when making predictions from unstructured data such as images or text.

When it comes to classifying images, deep learning shines by analyzing data not one-dimensionally, but two-dimensionally, using convolutional neural networks, or CNNs for short.

Convolutional neural networks are among the strongest machine learning algorithms in the world today for classifying images. In this section, you will learn the basic theory behind convolutions before building your own CNN.

MNIST

MNIST is the name of a famous dataset of handwritten digits from 1998 that has been widely used in computer vision. The dataset consists of 60K training images and 10K test images.

Google Colab includes a smaller sample of 20K training images, along with the 10K test images, that may be directly accessed in a Colab notebook and prepared for machine...

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