NEW: Semantic Reactor has been officially released!
Add it to Google Sheetshere.
It would be nice, then, if machine learning could help bridge the gap between the two.
It’s free, every week, in your inbox.
In machine learning, embeddings are a learned way of representing data in space (i.e.
points plotted on an n-dimensional grid) such that the distances between points are meaningful.
Check outthis neat visualization.
Where do these numbers come from?
Theyre learned by a machine learning model through data.
In particular, the model learns which words tend to occur in the same spots in sentences.
Consider these two sentences:
My mother gave birth to a son.
My mother gave birth to a daughter.
Word embeddings are extremely useful in natural language processing.
Using sentence embeddings, we can figure out if two sentences are similar.
When will you wake me up?)
It blew my mind.
If that sounds a little abstract, definitely watch the video I linked above.
It was just released, and you might add it to your Google Sheetshere.
a chatbot or an actor in a video game) might take.
), and sorts the results.
Youve transformed your responses and query into vectors.
Unfortunately, vectors are just points in space.
And thats it thats how you go from a Semantic ML spreadsheet to code fast!
She also likes solving her own life problems with AI, and talks about it on YouTube.
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