👉Consider this an ever-expanding thread for me to take notes + wrap my brain around products. Ready?
As a musician, Magenta makes me so dang happy.
You can map 8-button input to a full 88-key piano; automatically create melodic accompaniments; use machine learning to display visuals for music; transcribe tunes; generate new sounds; more.
This also doesn't get talked about *nearly* enough.
Seedbank is an ever-expanding collection of interactive machine learning examples that you can use, modify, experiment with, and grow to meet your needs+use case. research.google.com/seedbank/
A similar vein: most interpretability methods show importance weights in each input feature (e.g, pixel). TCAV instead shows importance of high level concepts (e.g., color, gender, race)—how humans communicate.
Two common use-cases within ML pipelines: (1) validation of continuously arriving data; (2) training/serving skew detection.
If you need batch splitting (data-parallel training), you probably don't need Mesh; but if you do intense distributed deep learning (ex: 5 billion parameters; a large # of activations that can't fit on one device; etc.), you should check it out!