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Hands-On Machine Learning with C++

You're reading from   Hands-On Machine Learning with C++ Build, train, and deploy end-to-end machine learning and deep learning pipelines

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781789955330
Length 530 pages
Edition 1st Edition
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Author (1):
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Kirill Kolodiazhnyi Kirill Kolodiazhnyi
Author Profile Icon Kirill Kolodiazhnyi
Kirill Kolodiazhnyi
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Overview of Machine Learning
2. Introduction to Machine Learning with C++ FREE CHAPTER 3. Data Processing 4. Measuring Performance and Selecting Models 5. Section 2: Machine Learning Algorithms
6. Clustering 7. Anomaly Detection 8. Dimensionality Reduction 9. Classification 10. Recommender Systems 11. Ensemble Learning 12. Section 3: Advanced Examples
13. Neural Networks for Image Classification 14. Sentiment Analysis with Recurrent Neural Networks 15. Section 4: Production and Deployment Challenges
16. Exporting and Importing Models 17. Deploying Models on Mobile and Cloud Platforms 18. Other Books You May Enjoy

ML model serialization APIs in C++ libraries

In this section, we will discuss the ML model sharing APIs in the Dlib, Shogun, Shark-ML, and PyTorch libraries. There are three main types of sharing ML models among the different C++ libraries:

  • Share model parameters (weights)
  • Share the entire model's architecture
  • Share both the model architecture and its trained parameters

In the following sections, we will look at what API is available in each library and emphasize what type of sharing it supports.

Model serialization with Dlib

The Dlib library uses the serialization API for decision_function and neural network type objects. Let's learn how to use it by implementing a real example.

First, we define the types for...

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