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

An overview of the RNN concept

The goal of an RNN is consistent data usage under the assumption that there is some dependency between consecutive data elements. In traditional neural networks, it is understood that all inputs and outputs are independent. But for many tasks, this independence is not suitable. If you want to predict the next word in a sentence, for example, knowing the sequence of words preceding it is the most reliable way to do so. RNNs are recurrent because they perform the same task for each element of the sequence, and the output is dependent on previous calculations.

In other words, RNNs are networks that have feedback loops and memory. RNNs use memory to take into account prior information and calculations results. The idea of a recurrent network can be represented as follows:

In the preceding diagram, a fragment of the neural network, (a layer of neurons...