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)
1
Section 1: Overview of Machine Learning
5
Section 2: Machine Learning Algorithms
12
Section 3: Advanced Examples
15
Section 4: Production and Deployment Challenges

What is deep learning?

Most often, the term deep learning is used to describe artificial neural networks that were designed to work with large amounts of data and use complex algorithms to train the model. Algorithms for deep learning can use both supervised and unsupervised algorithms (reinforcement learning). The learning process is deep because, over time, the neural network covers an increasing number of levels. The deeper the network is (that is, it has more hidden layers, filters, and levels of feature abstraction it has), the higher the network's performance. On large datasets, deep learning shows better accuracy than traditional machine learning algorithms.

The real breakthrough that led to the current resurgence of interest in deep neural networks occurred in 2012, after the publication of the article ImageNet classification with deep convolutional neural networks...