YouTube: https://www.youtube.com/watch?v=AU0O_6-EY6sTopics:00:00 Intro01:03 Yusuf’s background03:00 Multimodal search in tech and humans08:53 CLIP: discovering hidden semantics13:02 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 images32:05 Increasing efficiency: vector compression and quantization aspects34:25 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 internally51:19 Metric learning helps us learn the best vector representation for the given task52:16 Metric learning shines in data scarce regimes. Positive impact on the planet58:30 Yusuf’s motivation to work in the space of vector search, Qdrant, deep learning and metric learning — the question of Why1:05:02 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/DmitryKanFull Show Notes: https://www.youtube.com/watch?v=AU0O_6-EY6s

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