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

Delving into ONNX format

ONNX format is a special file format used to share neural network architectures and parameters between different frameworks. It is based on the Google Protobuf format and library. The reason why this format exists is to test and run the same neural network model in different environments and on different devices. Usually, researchers use a programming framework that they know how to use in order to develop a model, and then run this model in a different environment for production purposes or if they want to share their model with other researchers or developers. This format is supported by all leading frameworks, such as PyTorch, TensorFlow, MXNet, and others. But now, there is a lack of support for this format from the C++ API of these frameworks and at the time of writing, they only have a Python interface for dealing with ONNX format. Some time ago...