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Tag: algorithms

Mapping Global Cuisine with Word Embeddings

November 23, 2021Leave a comment

How can we systemically identify analogous food items across cultures? Learn how using word embeddings.

Self-Organizing Maps Tutorial

November 2, 2017November 3, 20178 Comments

Visualize large datasets and identify potential clusters with this special breed of neural networks that uses neurons to learn the intrinsic shape of your data.

k-Nearest Neighbors & Anomaly Detection Tutorial

September 14, 2016September 22, 20162 Comments

Do you know what gives red and white wine their colors? Use k-NN to discover the chemical make-up that defines typical types of wines, as well as to detect atypical ones.

Where Will Your Country Stand in World War III?

April 12, 2016September 22, 201613 Comments

Using weapons trade data, we map out who's against who in the complex arena of international politics.

Association Rules and the Apriori Algorithm

April 1, 2016September 22, 201610 Comments

You own a store. How do you discover purchasing patterns, such as which items tend to be bought together? Knowing this can improve your product placement and advertisement.

Convolutional Neural Networks (CNN) Introduction

January 26, 2016September 23, 20166 Comments

While an artificial neural network could learn to recognize a cat on the left, it would not recognize the same cat if it appeared on the right. To solve this problem, we introduce convolutional neural networks.

Multi-Arm Bandit & A/B Testing

January 14, 2016September 23, 20163 Comments

You want to publish ads for your product. While you have 2 promising ad designs, you have a limited budget. How can you find out which ad is more effective, while maximizing the impact of all the ads you publish?

K-Nearest Neighbor (KNN) Tutorial: Anomaly Detection

June 22, 2015September 23, 2016Leave a comment

Outliers can be detected by algorithms used for predictions. To illustrate, we use the k-nearest neighbor (kNN) clustering algorithm.

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About Algobeans

Algobeans is authored by Annalyn Ng (University of Cambridge) and Kenneth Soo (Stanford University). We noticed that while data science is increasingly used to improve workplace decisions, many people do not have a good understanding of how it works. Our goal is to support anyone who wants to get started in data science. Each tutorial covers the important functions and assumptions of a data science technique, without any math or jargon. We also illustrate these techniques with real-world data and examples.

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