Evaluating your machine learning models is a key component of all data science projects. Your model may produce some output with a high training accuracy, but that doesn’t mean that your output is meaningful. In the computer vision sphere, semantic and instance segmentation are often utilized to classify one or multiple objects in an image. These techniques are implemented using convolutional neural networks (CNN). Researchers and engineers use several metrics to ensure that their neural networks are actually producing quality results. In this article, I will discuss some of the most used segmentation evaluation metrics for binary classification. I will…


An overview of Convolutional Neural Networks.

Image processing is essential in a myriad of projects: creating autonomous vehicles, analyzing bioimaging data, developing facial recognition software. With new advancements in deep learning, researchers and corporations are taking advantage of the new automated methods utilized for image processing.

Convolutional Neural Networks (CNNs)

CNNs are a category of neural networks used when applying deep learning with visual data. They require much more computational power than most other neural network frameworks due to the sheer amount of data points and the dimensionality of the input data. CNNs essentially consist of multilayer perceptrons that are regularized to prevent overfitting. CNNs can be easily implemented through…


A scratch assay time series analysis.

Image processing is very applicable in present times. COVID-19 has brought upon new challenges for researchers across the globe, whether it be for developing a cure or to find new ways to detect the virus. In order to conduct research on COVID-19, researchers often need to perform heavy computations on images to detect specific elements that would be useful to draw new insights and conclusions.

In this article, I’ll go through the basics of image processing and a small tutorial detailing how image processing can be used for thresholding and segmentation, two very powerful techniques in image processing. …


A k-Nearest-Neighbor algorithm that I developed as my first data science project

If you’re a high school student, chances are you’re considering what to pursue for your future career. You might have interests in STEM subjects like math or biology, but you might not be sure which field to pursue. I was in the same position as many of you. I knew I wanted to get into the tech sector, but I had other interests in the physical sciences such as chemistry and physics. Then, I learned about data science and its interdisciplinary connection with several other STEM fields. …

Zeeshan Patel

I’m a freshman studying CS and Statistics at UC Berkeley. Feel free to contact me at zeeshanp@berkeley.edu.

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