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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Overview of Machine Learning
Section 1: Overview of Machine Learning
Free Chapter
2
Introduction to Machine Learning with C++
Introduction to Machine Learning with C++
Understanding the fundamentals of ML
An overview of linear algebra
An overview of linear regression
Summary
Further reading
3
Data Processing
Data Processing
Technical requirements
Parsing data formats to C++ data structures
Initializing matrix and tensor objects from C++ data structures
Manipulating images with the OpenCV and Dlib libraries
Transforming images into matrix or tensor objects of various libraries
Normalizing data
Summary
Further reading
4
Measuring Performance and Selecting Models
Measuring Performance and Selecting Models
Technical requirements
Performance metrics for ML models
Understanding the bias and variance characteristics
Model selection with the grid search technique
Summary
Further reading
5
Section 2: Machine Learning Algorithms
Section 2: Machine Learning Algorithms
6
Clustering
Clustering
Technical requirements
Measuring distance in clustering
Types of clustering algorithms
Examples of using the Shogun library for dealing with the clustering task samples
Examples of using the Shark-ML library for dealing with the clustering task samples
Examples of using the Dlib library for dealing with the clustering task samples
Plotting data with C++
Summary
Further reading
7
Anomaly Detection
Anomaly Detection
Technical requirements
Exploring the applications of anomaly detection
Learning approaches for anomaly detection
Examples of using different C++ libraries for anomaly detection
Summary
Further reading
8
Dimensionality Reduction
Dimensionality Reduction
Technical requirements
An overview of dimension reduction methods
Exploring linear methods for dimension reduction
Exploring non-linear methods for dimension reduction
Understanding dimension reduction algorithms with various С++ libraries
Summary
Further reading
9
Classification
Classification
Technical requirements
An overview of classification methods
Exploring various classification methods
Examples of using C++ libraries for dealing with the classification task
Summary
Further reading
10
Recommender Systems
Recommender Systems
Technical requirements
An overview of recommender system algorithms
Understanding collaborative filtering method details
Examples of item-based collaborative filtering with C++
Summary
Further reading
11
Ensemble Learning
Ensemble Learning
Technical requirements
An overview of ensemble learning
Examples of using C++ libraries for creating ensembles
Summary
Further reading
12
Section 3: Advanced Examples
Section 3: Advanced Examples
13
Neural Networks for Image Classification
Neural Networks for Image Classification
Technical requirements
An overview of neural networks
Delving into convolutional networks
What is deep learning?
Examples of using C++ libraries to create neural networks
Understanding image classification using the LeNet architecture
Summary
Further reading
14
Sentiment Analysis with Recurrent Neural Networks
Sentiment Analysis with Recurrent Neural Networks
Technical requirements
An overview of the RNN concept
Training RNNs using the concept of backpropagation through time
Exploring RNN architectures
Understanding natural language processing with RNNs
Sentiment analysis example with an RNN
Summary
Further reading
15
Section 4: Production and Deployment Challenges
Section 4: Production and Deployment Challenges
16
Exporting and Importing Models
Exporting and Importing Models
Technical requirements
ML model serialization APIs in C++ libraries
Delving into ONNX format
Summary
Further reading
17
Deploying Models on Mobile and Cloud Platforms
Deploying Models on Mobile and Cloud Platforms
Technical requirements
Image classification on Android mobile
Machine learning in the cloud – using Google Compute Engine
Summary
Further reading
18
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Anomaly Detection

Anomaly detection is where we search for unexpected values in a given dataset. An anomaly is a system behavior deviation or data value deviation from the standard value. There are other names for anomalies, such as outliers, errors, deviations, and exceptions. They can occur in data that's of diverse nature and structure as a result of technical failures, accidents, deliberate hacks, and more.

There are many methods and algorithms we can use to search for anomalies in various types of data. These methods use different approaches to solve the same problem. There are unsupervised, supervised, and semi-supervised algorithms. However, in practice, unsupervised methods are the most popular. The unsupervised anomaly detection technique detects anomalies in unlabeled test datasets, under the assumption that most of the dataset is normal. It does this by searching...

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