Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781789955330
Length 530 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Kirill Kolodiazhnyi Kirill Kolodiazhnyi
Author Profile Icon Kirill Kolodiazhnyi
Kirill Kolodiazhnyi
Arrow right icon
View More author details
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

Manipulating images with the OpenCV and Dlib libraries

Many machine learning algorithms are related to computer vision problems. Such tasks are object detection in images, segmentation, image classification, and others. To be able to deal with such tasks, we need instruments for working with images. We usually need routines to load images to computer memory, as well as routines for image processing. For example, the standard operation is image scaling, because many machine learning algorithms are trained only on images of a specific size. This limitation follows from the algorithm structure or is a hardware requirement. For example, we cannot load large images to the graphics processing unit (GPU) memory because of its limited size.

Also, hardware requirements can lead to a limited range of numeric types our hardware supports, so we will need to change initial image representation...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at R$50/month. Cancel anytime