Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter

Key Features

• Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing
• Cover interesting deep learning solutions for mobile
• Build your confidence in training models, performance tuning, memory optimization, and neural network deployment through every project

Book Description

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.

What you will learn

• Create your own customized chatbot by extending the functionality of Google Assistant
• Improve learning accuracy with the help of features available on mobile devices
• Perform visual recognition tasks using image processing
• Use augmented reality to generate captions for a camera feed
• Authenticate users and create a mechanism to identify rare and suspicious user interactions
• Develop a chess engine based on deep reinforcement learning
• Explore the concepts and methods involved in rolling out production-ready deep learning iOS and Android applications

Who this book is for

This book is for data scientists, deep learning and computer vision engineers, and natural language processing (NLP) engineers who want to build smart mobile apps using deep learning methods. You will also find this book useful if you want to improve your mobile app's user interface (UI) by harnessing the potential of deep learning. Basic knowledge of neural networks and coding experience in Python will be beneficial to get started with this book.

Osoby które kupowały "Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter", wybierały także:

• Konwolucyjne sieci neuronowe. Kurs video. Tensorflow i Keras w rozpoznawaniu obraz 149,00 zÅ‚, (44,70 zÅ‚ -70%)
• Data Science w Pythonie. Kurs video. Algorytmy uczenia maszynowego 199,00 zÅ‚, (59,70 zÅ‚ -70%)
• Machine Learning i jÄ™zyk Python. Kurs video. Praktyczne wykorzystanie popularnych bibliotek 249,00 zÅ‚, (74,70 zÅ‚ -70%)
• Web scraping w Data Science. Kurs video. Uczenie maszynowe i architektura splotowych sieci neuronowych 178,97 zÅ‚, (62,64 zÅ‚ -65%)
• Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zÅ‚, (67,05 zÅ‚ -55%)

Spis treści

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 eBook -- spis treści

• 1. Introduction to Deep Learning for Mobile
• 2. Mobile Vision : Face Detection using on-device models
• 3. Chatbot using Actions on Google
• 4. Recognizing Plant Species
• 5. Live Captions Generation of Camera Feed
• 6. Building Artificial Intelligence Authentication System
• 7. Speech/Multimedia Processing: Generating music using AI
• 8. Reinforced Neural Network based Chess Engine
• 9. Building Image Super-Resolution Application
• 10. Road Ahead
• 11. Appendix