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That being said, this is a sequel as well as a 101 in and of itself, meaning— like in the last article — that this will be mostly introductory. Go ahead to the end of this article if you want to download the data for yourself and follow along!
Today I want to solve a very popular NLP task called Named Entity Recognition (NER). In short, NER is a task of extracting Name Entities from a sequence of words (a sentence).
Why being able to interpret and measure a model’s output is crucial for improving it.
I have always been fascinated with signal processing, and facial recognition. I wanted to understand signal processing techniques on my own. As a result, I decided to attempt facial detection using only NumPy. I’m not claiming that the following algorithm is the optimal solution. The following guide documents my learning process
Gradient boosting (GB) is a machine learning algorithm developed in the late '90s that is still very popular. It produces state-of-the-art results for many commercial (and academic) applications. This page explains how the gradient boosting algorithm works using several interactive visualizations.