Hands-On Machine Learning with C++
Build, train, and deploy end-to-end machine learning and deep learning pipelines
Language - English
Updated on Oct, 2020
About the Book
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
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
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
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.

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Author Details

<a href="https://market.tutorialspoint.com/author/kirill_kolodiazhnyi">Kirill Kolodiazhnyi</a>
Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development.
Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge.
We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them.
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