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.


Is LaMDA Sentient/Sapient/Conscious?


It all depends on your definition(s) of sentience, sapience, and consciousness


Mimicking DeepMind's Chinchilla with GPT-3

Machine Learning

Short piece on using GPT-3 to reconstruct behaviors of one of DeepMind's new models


Until Next Time


A brief (on a cosmic scale) hiatus, to focus on writing a new book.


CLIP Prompt Engineering for Generative Art

Generative Art

A CLIP primer with 3500+ prompt output examples.


Messing with GPT-Neo

Machine Learning

How a reproduction of GPT-3 compares to the original, and how to use it.


Quantum Neural Blockchain AI for NFTs

April Fools

Just in time for April Fools Day: How to make NFT artwork for definitely-not-money-laundering (no artistic talent required).


Out-of-Distribution Taxonomy (preview)


Co-Authored by Vinay Prabhu

A preview of our upcoming guide of fixing out-of-distribution errors in your machine learning models