Destiny. Fate. Whatever you want to call it. The notion that everything that might happen to an entity is predetermined. People have been known to give their life to this concept, with the faith that whatever destiny has in store for them, will happen, no matter what. So why try? If the future is already written, why should I make an effort to build it? This kind of mentality is extremely, extremely dangerous, which is why it is necessary to clarify what destiny means for me. If we take the most common interpretation of it, destiny is what is behind an invisible and intangible curtain. It is a piece of paper that stretches all the way from our conception to our eventual demise. On the paper is written everything that has happened and everything that will. Everything that has happened is already in your mind, as memories, and whatever will happen is constantly entering your mind, again, as memories. These remembrances-to-come are not known to you until they have already happened. ...
Hating on music that is very popular in the world is really really common. Scorning artists like Drake, Taylor Swift, Sabrina Carpenter, Morgan Wallen, Adele, Billie Eilish, Maroon 5, One Direction, The Weeknd, is so quotidian now that it in itself has become mainstream. Yet so many people listen to them. Drake is raking in 41 million+ streams daily, Taylor Swift is accumulating 64 million streams every day and is on track to overtake Drake as the most streamed artist of all time. So clearly people are listening to these terrible, art-destroying, world-ending, glass-shattering artists. Numbers don’t lie. People do. The numbers show that the 200 decibel loud hate these mainstream artists get is just noise. People bash them from the outside, but secretly? They’re pressing play. The music’s good. That’s why so many people love it. So why do humans do this? Humans have a habit for rooting for the underdog. Always. No one ever roots for the king, they always root for the poor m...
This text details the technical workings of a BMI prediction application built using Streamlit. It uses a dataset of 526 individuals, and a Random Forest Regression model. The application leverages several machine learning techniques alongside the dlib library for facial landmark detection, aiming to deliver BMI estimates based solely on facial features. Data Preparation The core of this application is a dataset obtained from a publicly available source. This dataset contains facial measurements and corresponding BMI values, which serve as the foundation for training a predictive model. The dataset was preprocessed to exclude missing data, and relevant features were selected for the model. The features considered in the final model exclude demographic factors such as age and height, focusing on physical facial measurements derived from the images. These features were then scaled using StandardScaler to normalize the values, ensuring the model can learn effectively from the data w...
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