Book Image

Flutter Cookbook

By : Simone Alessandria, Brian Kayfitz
4 (1)
Book Image

Flutter Cookbook

4 (1)
By: Simone Alessandria, Brian Kayfitz

Overview of this book

“Anyone interested in developing Flutter applications for Android or iOS should have a copy of this book on their desk.” – Amazon 5* Review Lauded as the ‘Flutter bible’ for new and experienced mobile app developers, this recipe-based guide will teach you the best practices for robust app development, as well as how to solve cross-platform development issues. From setting up and customizing your development environment to error handling and debugging, The Flutter Cookbook covers the how-tos as well as the principles behind them. As you progress, the recipes in this book will get you up to speed with the main tasks involved in app development, such as user interface and user experience (UI/UX) design, API design, and creating animations. Later chapters will focus on routing, retrieving data from web services, and persisting data locally. A dedicated section also covers Firebase and its machine learning capabilities. The last chapter is specifically designed to help you create apps for the web and desktop (Windows, Mac, and Linux). Throughout the book, you’ll also find recipes that cover the most important features needed to build a cross-platform application, along with insights into running a single codebase on different platforms. By the end of this Flutter book, you’ll be writing and delivering fully functional apps with confidence.
Table of Contents (17 chapters)
16
About Packt
Machine Learning with Firebase ML Kit

Machine Learning (ML) has become a critical topic in any application's development. In a nutshell, ML means that you import data that "trains" an algorithm, and use it to generate a model. This trained model can then be used to solve problems that would be virtually impossible with traditional programming. To make this process more manageable, Firebase offers a service called ML Kit, an ML kit that we could define as "pre-built ML." Among other functionalities, it contains text recognition, image labeling, face detection, and bar-code scanning. For functionalities that are not already provided by ML Kit, you can create your own custom model with TensorFlow Lite. Most of the services outlined in this chapter can run both on the cloud or on your device. 

In this chapter, you will start by taking a picture with...