An Analysis of Normalized Cuts and Image Segmentation

In this paper, a detailed summary and analysis over Shi and Malik's paper on Normalized Cuts and Image Segmentation. Each section covers a summary and analysis of the respective portion of the original paper. The original paper can be found here: https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf In the introduction, Shi and Malik note that their research is based off of Wertheimer's Perceptual Grouping Theory.

2D Convolution using Python & NumPy

2D Convolutions are instrumental when creating convolutional neural networks or just for general image processing filters such as blurring, sharpening, edge detection, and many more. They are based on the idea of using a kernel and iterating through an input image to create an output image.

Understanding Generative Adversarial Networks Mathematically

Generative Adversarial Networks have become a large deal within the machine learning world. In this paper we take a look at the full mathematical source behind how generative adversarial networks work. We start from the very basics, giving an intuitive understanding and defining the different parts of a generative adversarial networks all the way up to understanding the loss function both mathematically and conceptually.

An Intuitive Introduction to Generative Adversarial Networks

With data becoming increasingly more important in the world of machine learning and data science, researchers have developed systems known to generate data from scratch. These systems are known as Generative Adversarial Networks or GANs. This paper gives a brief and intuitive introduction and analysis over Generative Adversarial Networks and their applications.

Intertwining COVID-19 & Software Development

With the rise of computer science in the world around us, the field of software development is seeming to have more of an impact on the medical field and COVID-19 than we originally thought.

Python Flask: The Guide to the Ultimate UI

Just Recently I was trying to create a UI for my facial recognition-based attendance tracker so that it would be easier for general users to use. I reasonably guessed based on some prior knowledge, that there would be some really nice library like how Java has JavaFX.

Facial Recognition Process

With computer vision technologies such as facial recognition coming to the forefront of many modern applications, the popularity of the technology rises. Despite the popularity, many fail to understand the underlying mechanisms that allow for facial recognition to work. Step 1: Pre-processing Data Sets The first major step in the facial recognition process is the preprocessing of data.

Expanding my Project

For those that have not seen my Github, I am currently in the works of developing a facial-recognition based attendance tracker. However, I not only want to develop this but I want to make it unique and innovative so it can be used for consumer use.

Computer Vision: CPU, GPU Allocation & Cloud Computing

Oftentimes, many computer enthusiasts throw around words such as CPU and GPU without realizing the significance or even meaning of these computational devices. So, to start, I would like to clarify a couple of concepts. A CPU is a central processing unit and it is often used to perform arithmetic computations and acts as the brain of the computer.

Transfer Learning

Recently I was trying to create a facial recognition-based attendance tracker for a high school project using small amounts of data because I did not have much data to work with, in the first place.