Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets
Key Features
Become familiar with data processing, performance measuring, and model selection using various C++ libraries
Implement practical machine learning and deep learning techniques to build smart models
Deploy machine learning models to work on mobile and embedded devices
Book Description
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.
What you will learn
Explore how to load and preprocess various data types to suitable C++ data structures
Employ key machine learning algorithms with various C++ libraries
Understand the grid-search approach to find the best parameters for a machine learning model
Implement an algorithm for filtering anomalies in user data using Gaussian distribution
Improve collaborative filtering to deal with dynamic user preferences
Use C++ libraries and APIs to manage model structures and parameters
Implement a C++ program to solve image classification tasks with LeNet architecture
Who this book is for
You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.
Table of Contents
Introduction to Machine Learning with C++
Data Processing
Measuring Performance and Selecting Models
Anomaly Detection
Dimensionality Reduction
Recommender Systems
Ensemble Learning
Neural Networks for Image Classification
Sentiment Analysis with Recurrent Neural Networks
Exporting and Importing Models
Deploying Models on Mobile and Cloud Platforms