Introduction

Overfitting or high variance in machine learning models occurs when the accuracy of your training dataset, the dataset used to “teach” the model, is greater than your testing accuracy. In terms of ‘loss’, overfitting reveals itself when your model has a low error in the training set and a higher error in the testing set. You can identify this visually by plotting your loss and accuracy metrics and seeing where the performance metrics converge for both datasets.

Overfitting indicates that your model is too complex for the problem that it is solving, i.e. your model has too many features in the case of regression models and ensemble learning, filters in the case of Convolutional Neural Networks, and layers in the case of overall Deep Learning Models. …


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Colorized x-ray image of a patient with pneumonia.

Why aren’t X-rays in color? The question that’s been on my mind for weeks while working to classify patients as having pneumonia or normal x-ray images from the Kaggle dataset. To me it seems to be a no brainer. An upgrade comparable to when television and movies went from black and white to color. According to MedlinePlus,

“The images show the parts of your body in different shades of black and white. This is because different tissues absorb different amounts of radiation. Calcium in bones absorbs x-rays the most, so bones look white. Fat and other soft tissues absorb less and look gray. …


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Chest X-rays depicting the lungs of pediatric patients. The areas annotated in blue show evidence of damage due to pneumonia.

In my previous post I built a classifier to train a machine decipher between the chest x-rays of pediatric patients with “normal” lungs and lungs showing signs of pneumonia damage. In this part of the blog we will get into some exploratory data analysis. X-rays are the top and most common method of detecting pneumonia worldwide. Why, you ask?

  • X-rays are a fast way to view internal organs & and air-filled parts
  • They are inexpensive (For Medical professionals)
  • There’s a fast turnaround time on results

However, the downsides of X-rays are that:

  • Images can be difficult decipher
  • It’s not possible to detect the type of bacteria or the source from a…

Recently, I tasked myself with analyzing chest x-ray images to determine whether or not a patient has pneumonia. The x-rays were provided by the Guangzhou Women and Children’s Medical Center in Guangzhou, China, on patients ranging from ages 1–5 years old. The image set contains images of “Normal”, “Viral” & “Bacterial” Pneumonia, classified into 2 categories: “Normal”and “Pneumonia”.

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Chest X-rays depicting the lungs of pediatric patients. The areas annotated in blue show evidence of damage due to pneumonia.

The dataset was provided in ideal conditions, free of poor quality scans. Before building the model, I checked the training data for class imbalance. There were 1341 Normal scans and 3875 Pneumonia scans. …


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Terry vs. Ohio (1967) the Supreme Court held that the search undertaken by the officer involved was reasonable because he “acted on more than just a hunch”, under the Fourth Amendment, which protects people against unreasonable search and seizure. Of the 43,077 people who were subjected to Terry Stops in Seattle, WA from 2015–2020, 10,505 of them were found to be carrying weapons. 9,474 subjects were frisked, and 2,173 subjects were arrested. This raises the question:

  • Why were the other 33,603 stopped?

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By now we’ve all seen the “We at *insert company here* are committed to Diversity & Inclusion…we stand in solidarity with the victims of police brutality…” PR templates, but what’s missing from these efforts are clear plans of action. Dismantling systemic racism involves more than just calling out police brutality; that is only one factor. Exclusive company cultures, implicit biases, hiring discrimination, are other factors where companies at all stages can use their resources to be actionable about systemic racism in the workplace. Starting with:

Culture: Culture is based on WHO you hire, not kombucha on tap and ping pong tables.


I’ve always known what I wanted. At 13 I decided that I wanted to pursue Mechanical Engineering, and at 16, I decided that I had to work for myself. I had gotten my first job, and was working 10–12 hours a shift. Doing grueling, menial tasks all day for a company that I didn’t care about, who’s mission I did not believe in. It motivated me to spend the remaining years of high school looking beyond what I would major in, and looking into companies where I can begin my career. Only I couldn’t find a company who was focusing on the social issues that I wanted to solve. I’d try to concoct ways of how I could persuade them to pivot in my direction, but the amount of red tape involved in raising a new idea or project, was just pure ridiculousness. I just couldn’t imagine spending my entire working career burning myself out to make someone else’s dream come true. …

Erica Gabriel

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