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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

An overview of neural networks

In this section, we will discuss what artificial neural networks are and their building blocks. We will learn how artificial neurons work and how they relate to their biological analogs. We will also discuss how to train neural networks with the backpropagation method, as well as how to deal with the overfitting problem.

A neural network is a sequence of neurons interconnected by synapses. The structure of the neural network came into the world of programming directly from biology. Thanks to this structure, the computer has the ability to analyze and even remember information. In other words, neural networks are based on the human brain, which contains millions of neurons that transmit information in the form of electrical impulses.

Artificial neural networks are inspired by biology because they are composed of elements with similar functionalities...

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