Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
Implement supervised and unsupervised machine learning (ML) algorithms using C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib with the help of real-world examples and datasets

Key Features

  • Familiarize yourself 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
  • Purchase of the print or Kindle book includes a free PDF eBook

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, 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. You’ll get hands-on experience with tuning and optimizing a model for different use cases, and get to grips with model selection and the measurement of performance. Next, you’ll cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib. You’ll also explore neural networks, deep learning, and transfer learning that allows you to use pre-trained models. The later chapters will teach you how to handle production and deployment challenges on mobile and cloud platforms, and how the ONNX model format can help you with such tasks. You’ll also learn how to extend existing deep learning frameworks with new operations. By the end of this book, you will have real-world ML and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

What you will learn

  • Find out how to load and pre-process various data types to suitable C++ data structures
  • Employ key machine learning algorithms with various C++ libraries
  • Understand how to find the best parameters for a machine learning model
  • Use anomaly detection for filtering user data
  • Apply collaborative filtering to deal with dynamic user preferences
  • Use C++ libraries and APIs to manage model structures and parameters
  • Build a C++ program for object detection with advanced neural networks
  • Extend machine learning frameworks with custom operators written in C++

Who this book is for

If you want to get started with machine learning algorithms and techniques using the popular C++ language, then this C++ machine learning book is for you. Aside from being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is needed to get started with this book.

1136968068
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
Implement supervised and unsupervised machine learning (ML) algorithms using C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib with the help of real-world examples and datasets

Key Features

  • Familiarize yourself 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
  • Purchase of the print or Kindle book includes a free PDF eBook

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, 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. You’ll get hands-on experience with tuning and optimizing a model for different use cases, and get to grips with model selection and the measurement of performance. Next, you’ll cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib. You’ll also explore neural networks, deep learning, and transfer learning that allows you to use pre-trained models. The later chapters will teach you how to handle production and deployment challenges on mobile and cloud platforms, and how the ONNX model format can help you with such tasks. You’ll also learn how to extend existing deep learning frameworks with new operations. By the end of this book, you will have real-world ML and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

What you will learn

  • Find out how to load and pre-process various data types to suitable C++ data structures
  • Employ key machine learning algorithms with various C++ libraries
  • Understand how to find the best parameters for a machine learning model
  • Use anomaly detection for filtering user data
  • Apply collaborative filtering to deal with dynamic user preferences
  • Use C++ libraries and APIs to manage model structures and parameters
  • Build a C++ program for object detection with advanced neural networks
  • Extend machine learning frameworks with custom operators written in C++

Who this book is for

If you want to get started with machine learning algorithms and techniques using the popular C++ language, then this C++ machine learning book is for you. Aside from being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is needed to get started with this book.

49.99 Pre Order
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
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

Paperback

$49.99 
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Overview

Implement supervised and unsupervised machine learning (ML) algorithms using C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib with the help of real-world examples and datasets

Key Features

  • Familiarize yourself 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
  • Purchase of the print or Kindle book includes a free PDF eBook

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, 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. You’ll get hands-on experience with tuning and optimizing a model for different use cases, and get to grips with model selection and the measurement of performance. Next, you’ll cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib. You’ll also explore neural networks, deep learning, and transfer learning that allows you to use pre-trained models. The later chapters will teach you how to handle production and deployment challenges on mobile and cloud platforms, and how the ONNX model format can help you with such tasks. You’ll also learn how to extend existing deep learning frameworks with new operations. By the end of this book, you will have real-world ML and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

What you will learn

  • Find out how to load and pre-process various data types to suitable C++ data structures
  • Employ key machine learning algorithms with various C++ libraries
  • Understand how to find the best parameters for a machine learning model
  • Use anomaly detection for filtering user data
  • Apply collaborative filtering to deal with dynamic user preferences
  • Use C++ libraries and APIs to manage model structures and parameters
  • Build a C++ program for object detection with advanced neural networks
  • Extend machine learning frameworks with custom operators written in C++

Who this book is for

If you want to get started with machine learning algorithms and techniques using the popular C++ language, then this C++ machine learning book is for you. Aside from being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is needed to get started with this book.


Product Details

ISBN-13: 9781805120575
Publisher: Packt Publishing
Publication date: 12/13/2024
Product dimensions: 75.00(w) x 92.50(h) x (d)

About the Author

Kirill Kolodiazhnyi is a seasoned software engineer with expertise in custom software development. He has several years of experience building machine learning models and data products using C++. He holds a bachelor degree in Computer Science from the Kharkiv National University of Radio-Electronics. He currently works in Kharkiv, Ukraine where he lives with his wife and daughter.

Table of Contents

Table of Contents

  1. Introduction to Machine Learning with C++
  2. Data Processing
  3. Measuring Performance and Selecting Models
  4. Clustering
  5. Anomaly Detection
  6. Dimensionality Reduction
  7. Classification
  8. Recommender Systems
  9. Ensemble Learning
  10. Neural Networks for Image Classification
  11. Sentiment Analysis with Recurrent Neural Networks
  12. Transfer learning
  13. Custom Operation creating
  14. Tracking and visualizing ML experiments
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