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

Measuring Performance and Selecting Models

This chapter describes the bias and variance effects and their pathological cases, which usually appear when training machine learning (ML) models. For example, the high variance effect, also known as overfitting, is a phenomenon in ML where the constructed model explains the examples from the training set but works relatively poorly on the examples that did not participate in the training process. This occurs because while training a model, random patterns will start appearing that are normally absent from the general population. The opposite of overfitting is known as underfitting. This happens when the trained model becomes unable to predict patterns in new data or even in the training data. Such an effect can be the result of a limited training dataset or weak model design.

In this chapter, we will learn how to deal with overfitting...