Hello developers, and welcome back.
Now let's start building our Android application.
Open Android Studio, select a new project, empty compose activity and name it as House price ML app.
Select Kotlin DSL, click finish and wait for Android Studio to configure your application.
Now let's go to Google, open Chrome, go to Google and search for TensorFlow.
Android dependencies.
There are a lot of references you get, but I prefer the Gemini overview and the AI overview.
And here we get the dependency for our TensorFlow Lite library to utilize TensorFlow Lite models within
an Android app.
The primary dependency required is the TensorFlow Lite library.
This is typically included in your Android project's build.gradle file, the model level.
Okay, so copy this and always use the latest stable version in order to get the latest stable version,
go again to Google search for latest stable version TensorFlow and you get this response.
Um, the TensorFlow is 2.20.0.
The latest stable version of TensorFlow, which includes a TensorFlow Lite runtime now referred as Lite
RT.
So TensorFlow changed to Lite RT.
You can still use TensorFlow Lite, or you can use the Lite RT library.
Okay so here go to Android Studio build.gradle file.
This is the module level.
Scroll down and add the dependency here.
Change it to the latest version which is 2.20.
Wait for Android Studio to configure and then sync the project.
Click sync now.
TensorFlow Lite is Google's lightweight machine learning framework designed specifically for mobile
and edge devices.
It's optimized for low latency, small binary size, low memory usage, and multiple platform support
like Android, iOS, embedded Linux, and microcontrollers.
Okay, so with this line, we've added our TensorFlow to the app.
Also, you can add some other optional devices for specific Functionalities.
GPU delegate for hardware acceleration.
Use the device's GPU implementation.
We can add this.
Use the stable latest stable version.
Also, this requires a compatible GPU and is not supported on emulators.
Okay, so please pay attention.
TensorFlow Lite with Google Play Services and alternative Runtime Environment that utilizes Google Play
Services to deliver TensorFlow Lite runtime and models.
Also, there are a lot of things, and other libraries we're going to use in the next projects provides
pre-built APIs for common ML tasks like object detection, image classification, and others.
In the next videos we're going to use, and in the next project, we're going to use the other types
and other APIs from TensorFlow.
Also, we can use Firebase ML Model Downloader.
If you are using Firebase to deploy and manage your TensorFlow Lite models, you can use this.
But for now we're going to use pure TensorFlow okay.
So we've added TensorFlow to our application.