This is data science blog tag written by data scientist in MTI Technology AI Lab.
Handling Missing Data – Abstract The article discusses various types of missing data and how to handle them. We demonstrate how the prediction results are affected by the quality of missing data as well as the method of handle missing data through some experiments. Introduction – Handling Missing Data For any real data set, missing data …
Introduction to Healthcare Data Science (Overview) Healthcare analytics is the collection and analysis of data in the healthcare field to study determinants of disease in human populations, identify and mitigate risk by predicting outcomes. This post introduces some common epidemiological study designs and an overview of the modern healthcare data analytics process. Types of Epidemiologic …
Introduction Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can “interpolate”: By decoding the convex combination of the latent codes for two data points, the autoencoder can produce an output that semantically mixes characteristics …
Implementation for Adversarially Constrained Autoencoder Interpolation (ACAI) Read More »
Pytorch is a deep learning framework and a scientific computing package The scientific computing aspect of PyTorch’s is primarily a result PyTorch’s tensor library and associated tensor operations. That means you can take advantage of Pytorch for many computing tasks, thanks to its supporting tensor operation, without touching deep learning modules. Important to note that …
In this blog post, I am going to introduce one of the most intuitive algorithms in the field of Supervised Learning[1], the k-Nearest Neighbors algorithm (kNN). The original k-Nearest Neighbors algorithm The kNN algorithm is very intuitive. Indeed, with the assumption that items close together in the dataset are typically similar, kNN infers the output …
I. A Brief Overview Consider an example of a courtroom trial: A car company C is accused of not manufacturing environment-friendly vehicles. The average CO2 emission per car from different manufacturers based on a survey from the previous year is 120.4 grams per kilometer. But for a random batch of 100 cars produced at C’s …
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In recent years, there have been a few prominent examples of accidental bias in machine-learning applications, such as smartphones’ beauty filters (that essentially ended up whitening skin) [1] or Microsoft’s from-innocent-teen-to-racist-in-24-hours chatbot [2,3]. Examples such as these fell victim to inherently biased data being fed into algorithms too complex to allow for much transparency. Hidden bias continues …
Bias in Data Science – the Good, the Bad and the Avoidable !? Read More »
Motivation What makes computers useful for us is primarily the ability to solve problems. The procedure in which computers solve a problem is an algorithm. In the recent context of an increasing number of algorithms available for solving data-related problems, there is increasing demand for a higher level of understanding of algorithm’s performance for data …
Can you expand on ? I guess you would find that is quite easy to do. You can easily find that . How about the expansion of . It is no longer easy. It is no longer easy, isn’t it. However, if we use Binomial Theorem, this expansion becomes an easy problem. Binomial Theorem is a very intriguing topic …
