In this chapter, we discussed how to deploy machine learning models, especially neural networks, to mobile and cloud platforms. We examined that, on these platforms, we usually need a customized build of the machine learning framework that we used in our project. Mobile platforms use different CPUs, and sometimes, they have specialized neural network accelerator devices, so you need to compile your application and machine learning framework in regards to these architectures. The architectures that are used for cloud machines differ from development environments, and you often use them for two different purposes. The first case is to use powerful machine configuration with GPUs to accelerate the machine learning training process, so you need to build your application while taking the use of one or multiple GPUs into account. The other case is using a cloud machine for inference...
Hands-On Machine Learning with C++
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Hands-On Machine Learning with C++
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Overview of this book
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.
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Hands-On Machine Learning with C++
Table of Contents (19 chapters)
Preface
Section 1: Overview of Machine Learning
Free Chapter
Introduction to Machine Learning with C++
Data Processing
Measuring Performance and Selecting Models
Section 2: Machine Learning Algorithms
Clustering
Anomaly Detection
Dimensionality Reduction
Classification
Recommender Systems
Ensemble Learning
Section 3: Advanced Examples
Neural Networks for Image Classification
Sentiment Analysis with Recurrent Neural Networks
Section 4: Production and Deployment Challenges
Exporting and Importing Models
Deploying Models on Mobile and Cloud Platforms
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