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Hands-On Machine Learning with C++

You're reading from   Hands-On Machine Learning with C++ Build, train, and deploy end-to-end machine learning and deep learning pipelines

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781789955330
Length 530 pages
Edition 1st Edition
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Author (1):
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Kirill Kolodiazhnyi Kirill Kolodiazhnyi
Author Profile Icon Kirill Kolodiazhnyi
Kirill Kolodiazhnyi
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Overview of Machine Learning
2. Introduction to Machine Learning with C++ FREE CHAPTER 3. Data Processing 4. Measuring Performance and Selecting Models 5. Section 2: Machine Learning Algorithms
6. Clustering 7. Anomaly Detection 8. Dimensionality Reduction 9. Classification 10. Recommender Systems 11. Ensemble Learning 12. Section 3: Advanced Examples
13. Neural Networks for Image Classification 14. Sentiment Analysis with Recurrent Neural Networks 15. Section 4: Production and Deployment Challenges
16. Exporting and Importing Models 17. Deploying Models on Mobile and Cloud Platforms 18. Other Books You May Enjoy

Transforming images into matrix or tensor objects of various libraries

In most cases, images are represented in computer memory in an interleaved format, which means that pixel values are placed one by one in linear order. Each pixel value consists of several numbers representing a color. For example, for the RGB format, it will be three values placed together. So, in the memory, we will see the following layout for a 4 x 4 image:

rgb rgb rgb rgb
rgb rgb rgb rgb
rgb rgb rgb rgb
rgb rgb rgb rgb

For image processing libraries, such a value layout is not a problem, but many machine learning algorithms require different ordering. For example, it's a common approach for neural networks to take image channels separately ordered, one by one. The following example shows how such a layout is usually placed in memory:

r r r r   g g g g   b b b b
r r r r g g g g b b b b
r r r r g g...
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