Browsing: Data Science
How should we choose between label, one-hot, and target encoding?15 min read·20 hours agoWhy Do We Need Encoding?In the realm of machine learning, most algorithms demand…
Practical lessons from upgrading Bed-Reader, a bioinformatics library21 min read·10 hours agoRust and Python reading DNA data directly from the cloud — Source: https://openai.com/dall-e-2/. All other…
Enabling fast data development from big operational systemsPhoto by Benjamin Zanatta on UnsplashFor a data engineer building analytics from transactional systems such as ERP (enterprise resource…
Prompt EngineeringThe GroundingDino model encodes text prompts into a learned latent space. Altering the prompts can lead to different text features, which can affect the performance…
Strategies for efficiently managing dimension changes and data restatement in enterprise data warehousingImagine this, you are a data engineer working for a large retail company that…
Data comes in different shapes and forms. One of those shapes and forms is known as categorical data.This poses a problem because most Machine Learning algorithms…
I discovered the Himalayan Database a few weeks ago and decided to create a few “whimsical” visualizations based on this dataset. In two previous articles I…
On a scale from 1 to 10 how good are your data ingestion skills?Photo by Blake Connally on UnsplashData ingestion is a crucial step in data…
In the world of data and computer programs, the concept of Machine Learning might sound like a tough nut to crack, full of tricky math and…
Insights after two years in the industryExample of an encoder and a graph in the latent space (image by author)The scenario: a high-speed production line is…