Category: Articles

In this tutorial, we’ll create a car that controlled using a USB camera and a Raspberry Pi. We’ll use a wood plank as the car body, which will hold all the car’s components. The components are:

After fixing the component using cable ties, we’ll connect the Raspberry Pito the motors using its general purpose input output (GPIO) pins. Similar to a traffic sign, the motors will run when the dominant color in the captured images is green and stop when it’s red.

Continue reading Building a Vision-Controlled Car Using Raspberry Pi—From Scratch

In this piece, we’ll take a closer look at some of the top machine learning articles in 2019, as ranked by MyBridge.

According to the article:

Pluribus was tested against professional poker players. This included two winners of the World Series of Poker Event. Pluribus uses self-play to teach itself how to win. This happens without any examples or guidance. The self-play version used in Pluribus is an improved version of the iterative Monte Carlo CFR (MCCFR) algorithm.

Continue reading 2019’s Top Machine Learning Articles

Computer vision is a field in artificial intelligence based on giving machines an understanding of data from images, video frames, pixel manipulations, and others. In this article, we will look into four commonly used annotation tools for object detection/tracking. To demonstrate these, we will be annotating team players on the football pitch.

The following is an outline on what we will be looking into:

Continue reading Four Important Computer Vision Annotation Tools you Need to Know in 2020

Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. The objects can generally be identified from either pictures or video feeds.

Continue reading A 2019 Guide to Object Detection

Compression involves processing an image to reduce its size so that it occupies less space. There are already codecs, such as JPEG and PNG, whose aim is to reduce image sizes. There are two types of image compression; lossy and lossless.

In lossless compression, one can retrieve the original image data, while in lossy compression one cannot. As a point of reference, PNG is lossless and JPEG is lossy.

Continue reading A 2019 Guide to Deep Learning-Based Image Compression

With recent developments in big data, we’ve been given more access to high-dimensional data. Consequently, the performance of machine learning models has improved by a large margin.

On the other hand, there are significant noisy and useless features often collected or generated by different sensors and methods. These unneeded features not only influence a model’s accuracy, but they also can demand a lot of computational resources.

Continue reading Hands-on with Feature Selection Techniques: An Introduction

This will be a short and concise tutorial on how to build a facial recognition system with JavaScript, using faceapi.js built on Tensorflow.js; hence we won’t be interacting with Tensorflow.js directly.

This is what our final model output should look like

We want to build an ML system, that when given a specific input image, will tell us if a face in the input image is similar a face in our database. 😉

Continue reading Facial Recognition System with JavaScript

This write up is an excerpt from my recent research on transfer functions and machine learning operations. Throughout this post, I’ll basically be establishing the core principles of these two different concepts, and examine their relationship to each other.

I have always understood machine learning algorithms as a simple relationship between variable X and Y, where X is the input data and Y is the learning outcome. The general polynomial relationship between X and Y is bounded by the following function:

Continue reading Transfer Functions for Machine Learning, Simplified

In this post, we’re going to learn about the most basic regressor in machine learning—linear regression. Specifically, we’re going to walk through the basics with a practical example in Python, and learn about applying feature engineering to the dataset we work with.

This is a two-part article, and this part encompasses the basics of feature engineering and regression. In part 2, we’ll discuss gradient descent (coding it from scratch), regularization, and other relevant concepts.

Continue reading Understanding the Mathematics Behind Linear Regression

Heads up! This is an end-to-end series. In its three parts, I’m going to show you how to train, save, and deploy a recommender model. Specifically, you will understand how to get and process your data, build and train a neural network, package it in an application, and finally serve it over the internet for everyone to see and use.

Continue reading Build, Train, and Deploy a Book Recommender System Using Keras, TensorFlow.js, Node.js, and Firebase (Part 1)