Category: Articles

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

If you’ve read my earlier blogs centered on AutoML and machine learning on edge devices, you know how easy it is to train and test a custom ML model with little to no prerequisite knowledge.

However, just training an ML model isn’t enough. You also need to know how to use them to make predictions. Maybe you need to build a cross-platform app using tools like QT, or maybe you want to host your model on a server to serve requests via an API. This third blog in the series on training and running Tensorflow models in a Python environment covers just that!

Continue reading Using Google Cloud AutoML Edge Image Classification Models in Python

Note: The original research report on which this post is based can be found here

If you’re one of those people with a large number of GPU compute nodes in your basement, or if you work for Google, training neural networks in a short time for challenging tasks is probably easy for you.

Continue reading The Mobile Neural Network Lottery

K-Nearest Neighbors (KNN) is a supervised learning algorithm used for both regression and classification. Its operation can be compared to the following analogy:

To make a prediction, the KNN algorithm doesn’t calculate a predictive model from a training dataset like in logistic or linear regression. Indeed, KNN doesn’t need to build a predictive model. Thus, for KNN, there is no actual learning phase. That’s why it’s usually categorized as a lazy learning method.

Continue reading Understand the Fundamentals of the K-Nearest Neighbors (KNN) Algorithm

Pandas is an open-source Python library built on top of NumPy. Pandas is probably the most popular library for data analysis in Python. It allows you to do fast analysis as well as data cleaning and preparation. One amazing aspect of Pandas is the fact that it can work well with data from a wide variety of sources such as: Excel sheets, CSV files, SQL files, or even a webpage.

Continue reading Tips and Tricks for Data Analysis with Pandas

Due to the popularity of deep neural networks, many recent hardware platforms have special features that target deep neural network processing. The Intel Knights Mill CPU will feature special vector instructions for deep learning. The Nvidia PASCAL GP100 GPU features 16-b floating-point (FP16) arithmetic support to perform two FP16 operations on a single-precision core for faster deep learning computation.

Continue reading The 2 Types of Hardware Architectures for Efficient Training and Inference of Deep Neural Networks

It’s becoming increasingly easy to incorporate machine learning into your mobile app development toolkit, courtesy of platforms and dev tools such as Firebase’s ML Kit.

These tools conceal technical complexities by packaging pre-trained machine learning models for use. They also allow you to train and import your own models.

Continue reading “Text Recognition in Flutter Using Firebase’s ML Kit”

This tutorial is the 10th part in the series, ML Kit for Mobile Developers. If you’re not quite caught up, you can start here:

ML Kit’s Face detection API is one of the APIs that I left out from the ML Kit series (until now), mainly because I couldn’t find a proper use-case of this API.

Continue reading Blink detection on Android using Firebase ML Kit’s Face Detection API