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


In this chapter, we learned the basic principles of RNNs. This type of neural network is commonly used in sequence analysis. The main differences between the feedforward neural network types are the existence of a recurrent link; the fact it is shared across timestep's weights; its ability to save some internal state in memory; and the fact it has a forward and backward data flow (bidirectional networks).

We became familiar with different types of RNNs and saw that the simplest one has problems with vanishing and exploding gradients, while the more advanced architectures can successfully deal with these problems. We learned the basics of the LSTM architecture, which is based on the hidden state, cell state, and three types of gates (filters), which control what information to use from the previous timestep, what information to forget, and what portion of information...