While working with any kind of client app (be it mobile or desktop), you almost always need to have permanent storage in place that can allow you to store data, even when your application is stopped or closed by a user. Usually, such storage can be used to save the data from your app’s last use, or for saving data that was fetched from the internet so you don’t need to fetch it again, etc.
Continue reading Abstracting SQL queries with Room Database in Android
Over the last few years, Apple has been pushing hard in the augmented reality department. From a simple, finicky API a few years ago, ARKit has grown into a seamless integration between cutting edge hardware and advanced software. With the introduction of ARKit 4 during WWDC20, augmented reality connects virtual objects to the real world better than ever. In this article, you’ll learn about the latest from Apple in augmented reality.
Continue reading ARKit 4: Putting the “Reality” Back in Augmented Reality
Hello readers, continuing on from my previous article where I explained about how the face recognition internally works, here I will give you the code implementation and will guide you step-by-step on how to start an attendance system using face recognition.
This whole attendance system can be divided into two parts, one where we will register students and the other which will be used to infer on a video feed. I will provide a basic working pipeline which can be extended to any use case easily.
Continue reading Attendance System Based On Face Recognition (Implementation)
In recent years, there’s been a steep increase in the number of write-ups and articles on ‘Artificial Intelligence’ (AI), ‘Machine Learning’ (ML) and ‘Big Data’—obviously because practical applications of these new technologies is trending upward in all business domains and in day-to-day life.
Oftentimes, online conversations centered on these technologies tend to interchange these terms, which is understandable given a lack of technical expertise. But somewhat surprisingly, quite a few tech-savvy authors and articles I’ve come across are also swapping these terminologies with each-other, despite the fact that AI, ML and big data are quite distinct from each other.
Continue reading Artificial Intelligence (AI) vs Machine Learning (ML) vs Big Data
As developers, we love exploring concepts and mechanisms while working with a new framework. As a cross-platform development framework, React Native has matured quite a lot since I started playing around with it. Understanding the fundamentals when learning it is something very helpful and—I believe—essential.
Thus, by applying the basic fundamentals of React Native knowledge, I’m going to walk you through how to build a to-do list application using an offline storage functionality. This storage functionality is provided by a native module in React Native called AsyncStorage.
Continue reading Building offline React Native apps with AsyncStorage
Every time you want to search for an image similar to another one, you’re more or less left with one option: “Search Google for image”. Have you ever wondered what algorithms are making this happen? This is what we’ll explore in this blog post.
There are numerous algorithms present today for this task, and depending on the use cases, the algorithms used can vary. Below are the major categories of image search engines:
Continue reading Building a Content-based Image Search Engine (Part 1)
Speaking to your home is no longer a sci-fi fantasy but a common part of everyday life for many. This is undoubtedly exciting, but the accessibility of voice assistants (Amazon Alexa, Google Assistant, etc…) is now more crucial than ever. In fact, assisting someone with a disability has a bigger positive impact than helping someone set a timer while baking.
In the first part of the series, we dealt extensively with text-preprocessing using NLTK and some manual processes; defining our model architecture; and training and evaluating a model, which we found good enough to be deployed based on the dataset we trained the model on.
Our next step is to reproduce the essential processes in production so that are able to synchronize expected outputs on new text inputs. We’ll start by converting the Notebook into scripts and modules in a different project environment, with necessary versions of libraries and frameworks installed.
Continue reading Building a Conversational Chatbot with NLTK and TensorFlow (Part 2)
Imagine we want to predict the weather for a day based on historical data, or precipitation for that same date.
Weather forecasts consist of collecting as much data as possible—whether the current or historical state of the atmosphere in a given area (temperature, humidity, wind, and many more)—and using tools to measure atmospheric conditions and activity.
Given this data, a meteorologist then determines the future evolution of the atmosphere.
Continue reading “Weather Prediction iOS Application Using Flask API and AI”
With the release of TensorFlow 2.0 Alpha during the TensorFlow Developer Summit, we’d like to take a moment and look at how we can use it. One of the major updates in this version is the use of Keras as the high-level API and eager execution. If you’re familiar with Keras, you’ll find the code in this short tutorial to be very familiar. In this short tutorial, we’ll illustrate how you can use TensorFlow 2.0 to build a deep learning model.
