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With the immense growth of videos on the internet, it’s become really difficult to efficiently search amongst millions of them. When searching for an event query, users are often bewildered by the vast quantity of videos returned by search engines like Google. Exploring such results can be time-consuming and can also degrade the user experience.

Hence, we’ll be discussing ways to automate this process with machine learning-based video summarization techniques.

Continue reading Deep learning-based video summarization — A detailed exploration

Whether you’re looking to touch up a family portrait or create a surreal masterpiece, Superimpose X’s expansive photo editing toolkit will help you realize your vision. Superimpose X inspires creative approaches with 3 primary sets of editing tools: layers, masks, and effects like blurs, color corrections, smudges, and more.

Continue reading Community Spotlight: Superimpose X

We live in the world that’s still transitioning from inventions of the Renaissance to inventions of the modern technological era. A couple examples: We still exchange business cards during meetings but store our numbers on cell phone; we still receive paper bills for our electricity, gas, and other utilities but pay them through mobile accounts. The list goes on and on. With the growing advances in machine learning, many steps of these processes can be eradicated right away.

Continue reading Building Text Detection apps for iOS and Android using React Native

If you’re a fan of HBO’s Silicon Valley, you’ll remember when they launched a real AI-powered mobile app that classifies hotdogs from a given image (or not). Using Google’s Vision API, let’s try to recreate a working model of the application in React Native.

Google’s Vision API is a machine learning tool that classifies details from an image provided as an input. The process of these classifications is based on thousands of different categories that are included in pre-trained API models. The Vision API enables access to these pre-trained models via a REST API.

Continue reading Build a “Not Hotdog” clone with React Native

In this article, we will continue building production machine learning systems on GCP with a special focus on the design of hybrid ML systems which covers the following:

Building hybrid cloud machine learning models Kubeflow for hybrid cloud Optimize Tensorflow graphs for mobile

Continue reading “Building Production Machine Learning Systems on Google Cloud Platform”

Complex models such as deep neural networks are prone to overfitting because of their flexibility in memorizing the idiosyncratic patterns in the training set, instead of generalizing to unseen data.

Any modification we make to a learning algorithm that’s intended to reduce its generalization error but not its training error is called regularization. Keeping the model simple enough by using regularization techniques allows the network to generalize well on data points it hasn’t seen before.

Continue reading Deep Learning Best Practices: Regularization Techniques for Better Neural Network Performance

In this tutorial, let’s start using a backend service to add real-time features to the chat app. For our backend services, I’m going to use Firebase. We’ll look at how to install and configure Firebase SDK in a React Native app with the help of react-native-firebase module, as well as set up and configure email authentication. In order to follow this tutorial and future posts, you’ll need to have an active Firebase project.

Continue reading Chat app with React Native : Firebase User Authentication with react-native-firebase

Core ML is Apple’s framework for machine learning. With Core ML, everyone can use machine learning in their apps—as long as that app runs on an Apple platform, and Apple platforms only. Core ML cannot be used with Android, Windows, or on websites. This is very unfortunate because Core ML is such a great piece of technology.

Continue reading Deploying Core ML models using Vapor

This tutorial covers how to set up DeepLab within TensorFlow to train your own machine learning model, with a focus on separating humans from the background of a photograph in order to perform background replacement—also known as image segmentation.

More generally, this article might be a good introduction for beginners without a lot of code experience in learning how to:

Continue reading How to replace backgrounds in photos with machine learning

In a previous tutorial titled Building an Android Login System, we created an Android login system, which allows the user to register by their first name, last name, username, and password. A Flask server listens for requests from the user and inserts a new record in a MySQL database. After successful registration, the user can log in and be verified by the server.

This tutorial extends upon the previous tutorial to allow users to enter an email address while registering. To verify the ownership of the entered email address, an email is sent to this address with a verification code. The user copies the code sent in the email and pastes it into the application to complete the email verification process.

Continue reading “Email Verification for an Android App Registration System”