We plot with the plotcpp library, which is a thin wrapper around the gnuplot command-line utility. With this library, we can draw points on a scatter plot or draw lines. The initial step to start plotting with this library is creating an object of the Plot class. Then, we have to specify the output destination of the drawing. We can set the destination with the Plot::SetTerminal() method and this method takes a string with a destination point abbreviation. It can be the qt string value to show the operating system (OS) window with our drawing, or it can be a string with a picture file extension to save a drawing to a file, as in the code sample that follows. Also, we can configure a title of the drawing, the axis labels, and some other parameters with the Plot class methods. However, it does not cover all possible configurations available for gnuplot. In...
Hands-On Machine Learning with C++
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Hands-On Machine Learning with C++
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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.
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Hands-On Machine Learning with C++
Table of Contents (19 chapters)
Preface
Section 1: Overview of Machine Learning
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Introduction to Machine Learning with C++
Data Processing
Measuring Performance and Selecting Models
Section 2: Machine Learning Algorithms
Clustering
Anomaly Detection
Dimensionality Reduction
Classification
Recommender Systems
Ensemble Learning
Section 3: Advanced Examples
Neural Networks for Image Classification
Sentiment Analysis with Recurrent Neural Networks
Section 4: Production and Deployment Challenges
Exporting and Importing Models
Deploying Models on Mobile and Cloud Platforms
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