Machine Learning

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In a nutshell, Machine Learning is a way to give computers the powers of generalization. And that's what I try to do: put [seemingly complicated] things simply. My posts on Machine Learning (ML) focus mainly on recent cutting edge research, and how to make it accessible to everyone. I felt like many academic guides weren't accessible enough, so I strove to make my guides as clear and practically-focused as possible.

Unsure where to start? Here are some of my best / most popular posts:

Similar tags include Tensorflow, GANs, and Private Machine Learning.

Enjoy!

Gaussian KDE from scratch

Machine Learning

Understanding KDE inside and out

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Under-Investigated Fields List (Version 1.0)

Progress Studies

Nominations for fields that are not being researched as much as they should be

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Optimal Brain Damage

Machine Learning

Cutting away 80% of neurons in a neural network with no impact on accuracy

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Lessons from becoming a Machine Learning Engineer in 12 months, without a CS or Math degree

Machine Learning

A Guide to starting from scratch, based on my own experiences (WARNING: LONG READ)

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Turning my Coworker's Chihuahua into a Bear

Machine Learning

Demo of Few-Shot Unsupervised Image-to-Image Translation

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Machine Learning Meme Collection

April Fools

An increasingly necessary component of being engaged on ML Twitter

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How to approach algorithm problems

Programming

An example that's probably closer to what you're experiencing in coding interviews

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Using TPUs in Google Colab (properly)

Machine Learning

A simple way to use TPUs with minimal hardware optimization

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