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

Delving into convolutional networks

The MLP is the most powerful feedforward neural network. It consists of several layers, where each neuron receives its copy of all the output from the previous layer of neurons. This model is ideal for certain types of tasks, for example, training on a limited number of more or less unstructured parameters.

Nevertheless, let's see what happens to the number of parameters (weights) in such a model when raw data is used as input. For example, the CIFAR-10 dataset contains 32 x 32 x 3 color images, and if we consider each channel of each pixel as an independent input parameter for MLP, each neuron in the first hidden layer adds about 3,000 new parameters to the model! With the increase in image size, the situation quickly gets out of hand, producing images that users can't use for real applications.

One popular solution is to lower the...