Hands-On Machine Learning with C++:Build,train,and deploy end-to-end machine learning and deep learning pipelines


Hands-On Machine Learning with C++:Build,train,and deploy end-to-end machine learning and deep learning pipelines
by:Kirill Kolodiazhnyi
pages 页数:530 pages
Publisher Finelybook 出版社:Packt Publishing (15 May 2020)
Language 语言:English
ISBN-10 书号:1789955335
ISBN-13 书号:9781789955330

Book Description
Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API,Caffe2,Shogun,Shark-ML,mlpack,and dlib with the help of real-world examples and datasets
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.
What you will learn

Explore how to load and preprocess various data types to suitable C++ data structures
Employ key machine learning algorithms with various C++ libraries
Understand the grid-search approach to find the best parameters for a machine learning model
Implement an algorithm for filtering anomalies in user data using Gaussian distribution
Improve collaborative filtering to deal with dynamic user preferences
Use C++ libraries and APIs to manage model structures and parameters
Implement a C++ program to solve image classification tasks with LeNet architecture
Table of Contents
Preface
Section 1:Overview of Machine Learning
Chapter 1:Introduction to Machine Learning withC++
Chapter 2:Data Processing
Chapter 3:Measuring Performance and Selecting Models
Section 2:Machine Learning Algorithms
Chapter 4:Clustering
Chapter 5:Anomaly Detection
Chapter 6:Dimensionality Reduction
Chapter 7:Classification
Chapter 8:Recommender Systems
Chapter 9:Ensemble Learning
Section 3:Advanced Examples
Chapter 10:Neural Networks for Image Classification
Chapter 11:Sentiment Analysis with Recurrent Neural Networks
Section 4:Production and Deployment Challenges
Chapter 12:Exporting and Importing Models
Chapter 13:Deploying Models on Mobile and Cloud Platforms
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Index

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