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

Sentiment analysis example with an RNN

In this section, we are going to build a machine learning model that can detect review sentiment (detect whether a review is positive or negative) using PyTorch. As a training set, we are going to use the Large Movie Review Dataset, which contains a set of 25,000 movie reviews for training and 25,000 for testing, both of which are highly polarized.

First, we have to develop parser and data loader classes to move the dataset to memory in a format suitable for use with PyTorch.

Let's start with the parser. The dataset we have is organized as follows: there are two folders for the train and test sets, and each of these folders contains two child folders named pos and neg, which is where the positive review files and negative review files are placed. Each file in the dataset contains exactly one review, and its sentiment is determined by...