Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines - Image 1

£47.99

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

Name: Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
Brand: Packt Publishing
Price: 47.99 GBP
Availability: InStock

AI & Machine Learning

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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. Table of Contents Introduction to Machine Learning with C++ Data Processing Measuring Performance and Selecting Models Clustering Anomaly Detection Dimensionality Reduction Classification Recommender Systems Ensemble Learning Neural Networks for Image Classification Sentiment Analysis with Recurrent Neural Networks Exporting and Importing Models Deploying Models on Mobile and Cloud Platforms

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 1789955335
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 15 May 2020
Listed Since: 04 May 2020

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

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