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Sentiment analysis is one of the most popular natural language processing (NLP) applications in the business world. Also known as opinion-mining, it’s a subfield of NLP that analyzes texts and attempts to classify them as positive or negative.

In supervised learning, this would be called a classification problem, wherein the texts have already been labeled and we use these labels to train machine learning models in order to generalize and classify unseen datasets successfully.

Continue reading Evaluating Unsupervised Sentiment Analysis Tools Using Labeled Data

In this article, we’ll see how we can use the Luminoth library to detect objects in pictures or videos. Luminoth is an open source computer vision library built in Python and based on TensorFlow and Sonnet. This library was developed by Tryolabs. You can learn more about Luminoth and some of their other projects here:

Continue reading “Object Detection with Luminoth”

If you’ve ever done any MacOS or iOS development, you’ve also eventually had to deal with Xcode build settings.

So what are those and what do we know about them?

For a standard iOS project, there are roughly 500 build settings grouped into around 50 categories. These build settings control virtually every single aspect of how your app is built and packaged. At the very least, build settings are what make Debug build so different from Release build.

Continue reading Xcode Build Settings in Depth

In part 1, I introduced the field of Natural Language Processing (NLP) and the deep learning movement that’s powered it. I also walked you through 3 critical concepts in NLP: text embeddings (vector representations of strings), machine translation (using neural networks to translate languages), and dialogue & conversation (tech that can hold conversations with humans in real time). In part 2, I’ll cover 4 other important NLP techniques that you should pay attention to in order to keep up with the fast growing pace of this research field.

Continue reading The 7 NLP Techniques That Will Change How You Communicate in the Future (Part II)

Flutter has a rich pool of packages and widgets. You can achieve most things by either using the rich widget pool that Flutter offers or by using a package.

However, sometimes you might find yourself in situations that demand you write native Java code*.

In this fourth installment of our Flutter series, we’ll look at how you can write Java code that communicates with your Flutter application.

Continue reading Writing Native Java Code in Flutter for Android

Tree-based machine learning methods are among the most commonly used supervised learning methods. They are constructed by two entities; branches and nodes. Tree-based ML methods are built by recursively splitting a training sample, using different features from a dataset at each node that splits the data most effectively. The splitting is based on learning simple decision rules inferred from the training data.

Continue reading Understanding Tree-Based Machine Learning Methods

I’ve recently been working a lot with video data—in fact, I’d already shared some learnings on a few deep learning-based video summarization techniques, in a recent article published on Heartbeat.

While I searched a lot for research papers that dealt with video summarization, I came across a paper that dealt with creating these summaries in the simplest way possible—by using the subtitles of a given video. You can check out the paper here.

Continue reading Video Summarization Using Subtitles: A Simplified Approach

I’d been interested in the idea of learning machine learning for quite a while. This interest in the field started after I discovered ML as being a subfield of AI from an online forum. My understanding of AI before this was limited to what I watched in sci-fi movies, where AI is portrayed as an artificial human that could outperform real humans in intelligence, which I didn’t find interesting.

Continue reading Why I thought machine learning was boring

There were quite a few interesting announcements during WWDC 2020. Without a doubt, enhancements in SwiftUI 2.0 and Apple’s bold decision to move away from Intel for Mac in favor of in-house Silicon chips became the major talking points.

But that didn’t stop Apple from showcasing its computer vision ambitions once again this year. The Vision framework has been enhanced with some exciting new updates for iOS 14.

Continue reading What’s New in the Vision Framework in iOS 14