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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

Using an SDK/tools to build a model

We covered the preparation for using a pre-existing service-based deep learning model for the task at hand, to predict the species of plant present in a picture. We will be training an image classifier model on samples from five different species of flowers. The model will then try to determine the species to which any image of a flower might belong. However, such models are usually trained on a generally available dataset, and would not have the specificity that might be required at times—for example, in a scientific laboratory. Hence, you must learn how to build your own models for predicting the plant species.

This can be achieved either by training a model completely from scratch or, alternatively, by extending a previously existing model. The upside to training a model completely from scratch is that you have complete control over...

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