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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

Developing RNN-based models for music generation

In this section, we'll be developing a music generation model. We'll be using RNNs for that, and using the LSTM neuron model for the same. An RNN is different from a simple artificial neural network (ANN) in a very significant way—it allows the reuse of input between layers. 

While, in an ANN, we expect input values that enter the neural network to move forward and then produce error-based feedback to be incorporated into the network weights, RNNs make the input come back to the previous layers in loops several times. 

The following diagram represents an RNN neuron:

From the preceding diagram, we can see that the input after passing through the activation function in the neuron splits into two parts. One part moves forward in the network toward the next layer or output, while the other part is fed back...

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