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I recorded this introduction to embeddings (perhaps the most essential concept behind how LLMs.) But this is different from everything else you've seen: We will generate embeddings from scratch using a Siamese Network and a contrastive loss. This video is for 5 year olds.

Santiago

451,613 subscribers

68,976 Aufrufe • vor 2 Jahren •via X (Twitter)

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11 Kommentare

Profilbild von Santiago

Santiagovor 2 Jahren

Some people say that YouTube's video interface is better. If that's you, here is the link of this video over there:

Profilbild von Santiago

Santiagovor 2 Jahren

This is a great exercise. You can read all you want, watch videos all you want, and listen to others all you want. But nothing, *nothing* will teach you more than building things yourself. Especially when you start from first principles, and add complexity step by step.

Profilbild von Hesam

Hesamvor 2 Jahren

I love when ML concepts are explained intuitively Bookmarked 👌🏻

Profilbild von Roshini Fernando

Roshini Fernandovor 2 Jahren

About embeddings. Awesome 😊🔥

Profilbild von nocodedev ⚡

nocodedev ⚡vor 2 Jahren

How to transcribe an YouTube video and ask questions about it using local llm?

Profilbild von Geoff Kim

Geoff Kimvor 2 Jahren

This is immensely helpful. Thank you for your service. 🫡

Profilbild von teamtom

teamtomvor 2 Jahren

thank you for the video! i tried to run your notebook in Google Colab to dig deeper but unfortunaly something was not OK. first i did not touch your code just run cell by cell but it failed with some Keras code /1

Profilbild von Santiago

Santiagovor 2 Jahren

Not sure what's happening. I just rerun it on my computer (Keras 3.3.3). Here is the training process:

Profilbild von teck 🖖🏿

teck 🖖🏿vor 2 Jahren

Hi, 👋 What course do you recommend for junior ?

Profilbild von Justin Borgers

Justin Borgersvor 2 Jahren

I just have to say "Thank You" for this tutorial. You really have a talent for explaining complex concepts in a way that is easy to understand!

Profilbild von Santiago

Santiagovor 2 Jahren

🙏🏻

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