Book Image

Hands-On Machine Learning with C++

By : Kirill Kolodiazhnyi
Book Image

Hands-On Machine Learning with C++

By: Kirill Kolodiazhnyi

Overview of this book

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.
Table of Contents (19 chapters)
Section 1: Overview of Machine Learning
Section 2: Machine Learning Algorithms
Section 3: Advanced Examples
Section 4: Production and Deployment Challenges

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...