Episode description

Topics:

00:00 Intro

01:03 Yusuf’s background

03:00 Multimodal search in tech and humans

08:53 CLIP: discovering hidden semantics

13:02 Where to start to apply metric learning in practice. AutoEncoder architecture included!

19:00 Unpacking it further: what is metric learning and the difference with deep metric learning?

28:50 How Deep Learning allowed us to transition from pixels to meaning in the images

32:05 Increasing efficiency: vector compression and quantization aspects

34:25 Yusuf gives a practical use-case with Conversational AI of where metric learning can prove to be useful. And tools!

40:59 A few words on how the podcast is made :) Yusuf’s explanation of how Gmail smart reply feature works internally

51:19 Metric learning helps us learn the best vector representation for the given task

52:16 Metric learning shines in data scarce regimes. Positive impact on the planet

58:30 Yusuf’s motivation to work in the space of vector search, Qdrant, deep learning and metric learning — the question of Why

1:05:02 Announcements from Yusuf

- Join discussions at Discord: https://discord.qdrant.tech

- Yusuf's Medium: https://medium.com/@yusufsarigoz and LinkedIn: https://www.linkedin.com/in/yusufsarigoz/

- GSOC 2022: TensorFlow Similarity - project led by Yusuf: https://docs.google.com/document/d/1fLDLwIhnwDUz3uUV8RyUZiOlmTN9Uzy5ZuvI8iDDFf8/edit#heading=h.zftd93u5hfnp

- Dmitry's Twitter: https://twitter.com/DmitryKan

Full Show Notes: https://www.youtube.com/watch?v=AU0O_6-EY6s