Entity extraction can be useful when you want to add interactivity to your application based on the context of the text. For example, if it’s a phone number you can prompt the user to make a call and if it’s an email address you can prompt the user to open the email app. This is achieved by first extracting various entities in the text. In this piece, let’s look at how that can be achieved using Google’s ML Kit.
Let’s start by adding the internet permission to the Android Manifest because it’s needed in order to download the model.
Using Google’s ML Kit one can build an application that can be consumed in more than 50 languages. For example, you can have users select the language they prefer and translate the app’s content into that language. The translation is quite fast because it happens on the user’s device. The ML Kit translation models are built to translate to and from English. When making translations from another language, English will be used as the intermediate language during the translation process. This can definitely affect the quality of the translation. …
Once your machine learning model is ready, you have to deploy it to a device. One of the ways that can be done is by shipping the model with the application. A challenge with this method is that whenever your model changes, you will need to ship a new APK to the app stores. Obviously, this takes a long time because every app update needs to be verified by the app store. Now, imagine if it was possible to update the model over the air without the need to ship a new application. …
Using ML Kit, the language of a string of text can be determined. The ML Kit API supports over 100 languages. It can also identify native and romanized text from languages such as Russian and Arabic. Given a string of text, the API provides the most likely languages as well as the confidence level. Let’s look at how that can be done.
Start by adding the dependencies for the ML Kit Android libraries. Add the following in your
The application is made of a text input, a text view, and a button. The text input will be…
Using Google’s ML Kit, relevant replies to messages can be generated. The Smart Reply model generates replies based on the context of a conversation. The model generates useful reply suggestions because it uses the entire conversation. Hosting the model on the device ensures that users get reply suggestions quickly since no remote server is involved. The model currently supports only English. Therefore, if the conversation is in another language, no reply suggestions are generated. In this article, let’s look at how we can use this model.
Since the model will download on the device, internet permission is a requirement. …
Once your TensorFlow model is ready, you can easily deploy it to a mobile application. This is done by converting it to the TF Lite format. If you are working on a common task such as image classification and object detection, you can easily grab a pre-trained model from TensorFlow Hub. In this piece, we’ll use a pre-trained model to illustrate how one can deploy their model on an Android device.
In this article, we saw that we can use a pre-trained model from Fritz AI to perform pose estimation. Now, we will look at the steps we have to take in order to train a custom pose estimation model using Fritz Studio.
In this piece, we’ll train a model that is able to identify the right eye, left eye, and nose on a human face. A model that recognizes other body parts such as the elbow, ankle, etc., can also be trained, however, that requires more training images as well as more training time. For simplicity’s sake, we will use…
In this article, we’ll train a model that is able to label cat and dog images. The images used for this project can be found here and here. Of course, you don’t have to use cats and dogs; the process is the same irrespective of your images. Before we get too far into this, you’ll first need to create an account at Fritz AI.
Once you are logged in, click create a project on the left panel. Then make sure you select Custom Trained in order to get the option to train your own model.
Of the most time-consuming tasks in computer vision is obtaining and processing images. Obviously, a large dataset helps to improve the accuracy of your computer vision model. What if I told you that you can generate synthetic images at the click of a button? Using Fritz Studio, you can generate hundreds of training images from just a dozen seed images. To top it off, Fritz Studio will also allow you to train a model of your choice and download the model. If that sounds interesting to you, read on.
To help sift through some of the incredible projects, research, demos, and more in 2020, here’s a look at 17 of the most popular and talked-about projects in machine learning, curated from the r/MachineLearning subreddit. I hope you find something inspiring, educational, or both on this list.
This is an AR+ML software that allows you to cut elements from surroundings and paste them in image editing software.
Google Developer Expert — Machine Learning