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

Examples of using the Shogun library for dealing with the clustering task samples

The Shogun library contains implementations of the model-based, hierarchical, and partition-based clustering approaches. The model-based algorithm is called GMM (Gaussian Mixture Models), the partition one is the k-means algorithm, and hierarchical clustering is based on the bottom-up method.

GMM with Shogun

The GMM algorithm assumes that clusters can be fit to some Gaussian (normal) distributions; it uses the EM approach for training. There is a CGMM class in the Shogun library that implements this algorithm, as illustrated in the following code snippet:

     Some<CDenseFeatures<DataType>> features;
int num_clusters = 2;