Image Source: Photo by Jon Tyson on Unsplash

Understanding how historical data can lead to algorithmic bias taking a naive example of a compensation prediction model

To be human is to be Biased?

“Bias” is a tendency or inclination to favor or disfavor a certain set over the other. All humans have a certain degree of bias because we are inherently programmed to discern anyone different as a threat. Due to this implicit bias, we tend to unconsciously ascribe traits and qualities to usually stigmatized groups in society. These include groups based on sex, race, nationality, or geographic heritage. According to Stanford Encyclopedia of Philosophy-

“Research on ‘implicit bias’ suggests that people can act on the basis of prejudice and stereotypes without intending to do so”

Now if we know and understand this…


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A look into how we used Shopify, Google Analytics & Klaviyo data to increase the Customer Lifetime Value for a Direct-to-consumer E-commerce Start-up.

Where is this going?

Start-ups are engaged in a juggling-act: focusing on high-growth, which Reid Hoffman refers to as blitzscaling, or increasing profitability. A common way to achieve the former is to engage in price-wars and gather more customers. However, the latter goal is a pithy one; centered around the customer lifetime value or CLV.


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A look into a time distributed deep bimodal approach to predict scores for the Big-5 Personality traits based on videos from the First Impression Challenge on Google Colab.

Videos are the New First Impressions!

Think about the approximate number of video calls you have been a part of since March, 2020. Now, compare it to the number of video calls you were a part of before that. I am sure the difference is huge for most of us. Meetings with family, friends, and colleagues have shifted to video calls.

Video calling has also made it possible for us to keep expanding our networks and meet new people while maintaining social distancing. Hence, it is not wrong to say that, we are making quite a few personal as well as professional first impressions over videos…


A look into the need to balance overfitting and underfitting with data augmentation using an application of Image segmentation on satellite images to identify water bodies.

The Effect of Data Augmentation

When training neural networks, data augmentation is one of the most commonly used pre-processing techniques. The word “augmentation” which literally means “the action or process of making or becoming greater in size or amount”, summarizes the outcome of this technique. But another important effect is that it increases or augments the diversity of the data. The increased diversity means, at each training stage the model comes across a different version of the original data.

By Author

Why do we need this ‘increased diversity’ in data? The answer lies in the core tenet of machine learning — The Bias-Variance tradeoff. More complex models…


Comparing the denoising performance of Autoencoders with residual networks across the bottleneck to those without on a sample of RGB images from Flickr.

Autoencoders:

The official Keras blog, calls autoencoders an example of ‘self-supervised’ algorithms as their targets are generated from the input data. Hence, they are used for tasks of image reconstruction.

The main parts of an autoencoder are: Encoder, Bottleneck and Decoder. The Encoder is extracts image features at each step and in the process compresses the input data. The bottleneck constrains the input to its lowest dimensions known as compressed representations of the input data. The Decoder comes after this bottleneck and is used to reconstruct the input data. …


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How Transfer Learning gives a head start with limited data and time

Note from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here.

The year 2020 has witnessed the outbreak of the pandemic, COVID-19 which has brought the entire world to a standstill. The scientific community has been continuously working towards getting a medical breakthrough for a potential cure. It has become a race against the quick spread of this…

Metika Sikka

MSBA student at Columbia University

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