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

Understanding natural language processing with RNNs

Natural language processing (NLP) is a subfield of computer science that studies algorithms for processing and analyzing human languages. There are a variety of algorithms and approaches for teaching computers to solve a task that assumes using human language data. Let's start with the basic principles used in this area. After all, the computer does not know how to read, so the first issue with NLP is that you have to teach a machine to work with natural language words. One idea that comes to mind is to encode words with numbers in the order they exist in the dictionary. This idea is fairly simple numbers are endless, and you can number and renumber words with ease. But this idea has a significant drawback; the words in the dictionary are in alphabetical order, and when we add new words, we need to renumber a lot...