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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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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

Understanding natural language processing with RNNs

Natural language processing (NLP) is a subfield of computer science that studies algorithms for processing and analyzing human languages. There are a variety of algorithms and approaches for teaching computers to solve a task that assumes using human language data. Let's start with the basic principles used in this area. After all, the computer does not know how to read, so the first issue with NLP is that you have to teach a machine to work with natural language words. One idea that comes to mind is to encode words with numbers in the order they exist in the dictionary. This idea is fairly simple numbers are endless, and you can number and renumber words with ease. But this idea has a significant drawback; the words in the dictionary are in alphabetical order, and when we add new words, we need to renumber a lot...

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