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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Speech/Multimedia Processing - Generating Music Using AI

Given the increasing number of applications of artificial intelligence (AI), the idea of using AI with music has been around for a long time and is much researched. Since music is a series of notes, it is a classic example of a time series dataset. Time series datasets have proven very useful recently in a number of forecast areas – stock markets, weather patterns, sales patterns, and other time-based datasets. Recurrent neural networks (RNNs) are one of the most models for working with time series datasets. A popular enhancement made to RNNs is called long short-term memory (LSTM) neurons. We'll be using LSTMs in this chapter to work with the music notes.

Multimedia processing, too, isn't a new topic. Earlier in this project series, we covered image processing in detail in multiple chapters. In this...

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