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)
1
Section 1: Overview of Machine Learning
5
Section 2: Machine Learning Algorithms
12
Section 3: Advanced Examples
15
Section 4: Production and Deployment Challenges

Understanding the bias and variance characteristics

The bias and variance characteristics are used to predict model behavior. They are universal keywords. Before we go any further and describe what they mean, we should consider validation. Validation is a technique that's used to test model performance. It estimates how well the model makes predictions on new data. New data is data that we did not use for the training process. To perform validation, we usually divide our initial dataset in two or three parts. One part should contain most of the data and will be used for training, while other ones will be used to validate and test the model. Usually, validation is performed for iterative algorithms after one training cycle (often called an epoch). Alternatively, we perform testing after the overall training process.

The validation and testing operations evaluate the model...