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Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter
- 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
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.
Table of Contents
- Introduction to Deep Learning for Mobile
- Mobile Vision : Face Detection using on-device models
- Chatbot using Actions on Google
- Recognizing Plant Species
- Live Captions Generation of Camera Feed
- Building Artificial Intelligence Authentication System
- Speech/Multimedia Processing: Generating music using AI
- Reinforced Neural Network based Chess Engine
- Building Image Super-Resolution Application
- Road Ahead
Use the power of deep learning with Python to build and deploy intelligent web applications
- Create next-generation intelligent web applications using Python libraries such as Flask and Django
- Implement deep learning algorithms and techniques for performing smart web automation
- Integrate neural network architectures to create powerful full-stack web applications
When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python.
Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages.
By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices.
What you will learn
- Explore deep learning models and implement them in your browser
- Design a smart web-based client using Django and Flask
- Work with different Python-based APIs for performing deep learning tasks
- Implement popular neural network models with TensorFlow.js
- Design and build deep web services on the cloud using deep learning
- Get familiar with the standard workflow of taking deep learning models into production
Who this book is for
This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.
Table of Contents
- Demystifying Artificial Intelligence and Fundamentals of Machine Learning
- Getting Started with Deep Learning Using Python
- Creating Your First Deep Learning Web Application
- Getting Started with TensorFlow.js
- Deep Learning through APIs
- Deep Learning on Google Cloud Platform Using Python
- DL on AWS Using Python: Object Detection and Home Automation
- Deep Learning on Microsoft Azure Using Python
- A General Production Framework for Deep Learning-Enabled Websites
- Securing Web Apps with Deep Learning
- DIY - A Web DL Production Environment
- Creating an E2E Web App Using