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 image classification using the LeNet architecture

In this section, we'll implement a convolutional neural network for image classification. We are going to use the famous dataset of handwritten digits called the Modified National Institute of Standards and Technology (MNIST), which can be found at The dataset is a standard that was proposed by the US National Institute of Standards and Technology to calibrate and compare image recognition methods using machine learning, primarily based on neural networks.

The creators of the dataset used a set of samples from the US Census Bureau, with some samples written by students of American universities added later. All the samples are normalized, anti-aliased grayscale images of 28 x 28 pixels. The MNIST database contains 60,000 images for training and 10,000 images for testing. There...