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!

Presenting at the ACAI 2020 Workshop

How to hold an AI conference in the COVID-19 era

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Messing with GPT-3

Machine Learning

Why OpenAI's GPT-3 doesn't do what you think it does, and what this all means

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Nitpicking Machine Learning Technical Debt

Paper Critiques

Revisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)

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Building a better Patent Classifier

Tutorials

Building a classifier to tell you whether your LIDAR patent will be approved

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Gaussian KDE from Scratch

Machine Learning

Understanding KDE inside and out

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Style Transfer with Binarized Neural Networks

Co-Authored by Vinay Prabhu

Neural Style Transfer with binary 0s and 1s instead of floating points in your ImageNet model

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Structural Similarity in Neural Style Transfer

Co-Authored by Vinay Prabhu

Bringing back an ancient computer vision technique to effortlessly improve style transfer results

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