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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By : Anubhav Singh, Bhadani
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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

1 (1)
By: Anubhav Singh, Bhadani

Overview of this book

Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.
Table of Contents (13 chapters)
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Summary

In this chapter, we learned about the growth of AI in mobile devices, which provides machines with the ability to reason and make decisions without being explicitly programmed. We also studied machine learning and deep learning, which are inclusive of the technologies and algorithms associated with the domain of AI. We looked at various deep learning architectures, including CNNs, GANs, RNNs, and LSTMs.

We introduced reinforcement learning and NLP, along with the different methods of integrating AI on Android and iOS. Basic knowledge of deep learning and of how we can integrate it with mobile apps is important for the upcoming chapters, where we shall be extensively using this knowledge to create some real-world applications.

In the next chapter, we will learn about face detection using on-device models.

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