Category: Articles

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.

Continue reading Building a Voice Assistant for Blind and Partially-Sighted People: A Student Group Project

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.

Continue reading Working with TensorFlow 2.0 Alpha

Back in September, NVIDIA announced that it was set to acquire the chip designer giant ARM for a whopping $40 billion. This is kind of a big deal taking into account that ARM architecture is virtually everywhere these days, from top smartphone manufacturers such as Samsung and Apple utilizing it, to gaming consoles, home appliances, and various IoT devices that make use of a system-on-a-chip (SOC) design model. The business deal, currently under the processes of regulatory approval, is set to make ARM a subsidiary of NVIDIA while retaining its operational strategies and licensing partnerships, such as with Qualcomm, Samsung, Apple, and others.

Continue reading Will NVIDIA GPUs push AI on mobile devices to the next level?

It’s widely speculated that Apple will at some point transition the Mac platform from Intel-based processors to the very same ARM-based systems that power the iPhone and iPad.

For some, this possibility is more than just speculation, but fact. And that the only real questions are simply when will Apple make the jump?

Continue reading How Mac on ARM Will Power Future AI/ML Development

In data science competitions and machine learning projects, we often may encounter geospatial features that are (most of the time) represented as longitude and latitude.

These kinds of features will influence your predictive model’s results by a large margin if they aren’t well represented; therefore, these features are seldom considered, and they’re often eliminated from the feature’s set.

Continue reading Working with Geospatial Data in Machine Learning