Hands-On Machine Learning with C++

Build, train, and deploy end-to-end machine learning and deep learning pipelines

Kirill Kolodiazhnyi 지음

Packt(GCO Science) 2020년 05월 15일 출간

작품소개

책소개

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

▶Book Description
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

▶Key Features
？ Become familiar with data processing, performance measuring, and model selection using various C++ libraries
？ Implement practical machine learning and deep learning techniques to build smart models
？ Deploy machine learning models to work on mobile and embedded devices

▶Who This Book Is For
You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.

목차

▶TABLE of CONTENTS
▷ Section 1: Overview of Machine Learning
1. Introduction to Machine Learning with C++
2. Data Processing
3. Measuring Performance and Selecting Models

▷ Section 2: Machine Learning Algorithms
4. Clustering
5. Anomaly Detection
6. Dimensionality Reduction
7. Classification
8. Recommender Systems
9. Ensemble Learning

▷ Section 3: Advanced Examples
10. Neural Networks for Image Classification
11. Sentiment Analysis with Recurrent Neural Networks

▷ Section 4: Production and Deployment Challenges
12. Exporting and Importing Models
13. Deploying Models on Mobile and Cloud Platforms

출판사 서평

▶ Preface
Machine learning (ML) is a popular approach to solve different kinds of problems. ML allows you to deal with various tasks without knowing a direct algorithm to solve them. The key feature of ML algorithms is their ability to learn solutions by using a set of training samples, or even without them. Nowadays, ML is a widespread approach used in various areas of industry. Examples of areas where ML outperforms classical direct algorithms include computer vision, natural language processing, and recommender systems.

This book is a handy guide to help you learn the fundamentals of ML, showing you how to use C++ libraries to get the most out of data. C++ can make your ML models run faster and more efficiently compared to other approaches that use interpreted languages, such as Python. Also, C++ allows you to significantly reduce the negative performance impact of data conversion between different languages used in the ML model because you have direct access to core algorithms and raw data.

작가정보

저자(글) Kirill Kolodiazhnyi