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Top /r/MachineLearning Posts, Feb 8-14: Automating Tinder, Statistics and Machine Learning


Automating Tinder with Eigenfaces, statistics lessons in big data analysis, an upcoming AMA, the basics of PCA, and neural network programming in Python are all topics covered in the last week on Reddit.



By Grant Marshall.

Tinderbox message tree This week on /r/MachineLearning there are some interesting posts ranging from applications to dating apps, an AMA announcement, and tutorials on practical ML.

1. Automating Tinder with Eigenfaces +192

This post explores using Eigenfaces in the context of Tinder, the dating app. (also published on KDnuggets: Tinderbox: Automating Romance with Tinder and Eigenfaces)
In it, the author creates a system that automatically interacts with the Tinder app using k-nearest neighbors on an Eigenface of the user’s preferred match to decide who to match with. All of the code is open source, so feel free to peruse it here.

2. 10 things statistics taught us about big data analysis (or machine learning) +82

This post actually links back to KDnuggets. This post details how lessons from statistics apply well to big data analysis, and if you missed it when it was posted last week, be sure to give it a read.

3. Juergen Schmidhuber will be doing an AMA in /r/MachineLearning on March 4 10AM EST +82

This is an announcement for the upcoming AMA (ask me anything) with Juergen Schmidhuber. He is the director of the Swiss AI Lab IDSIA and a professor of AI at the University of Lugano. If you have any questions for him, visit on March 4th and get a chance to engage with him!

4. Principal Component Analysis explained visually +65

This article goes through and explain PCA primarily through visual aids. If you find that this helps you learn, I’d recommend the article as a good introduction to the topic.

5. A very gentle tutorial on a very basic neural network in python. +64

This tutorial introduces the topic of neural networks using numpy in Python. This can teach you the very basics of neural networks and connect those basics to some code. If you like to learn theoretical topics alongside the code, this is a good start.

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