[Day 100] Embeddings in practice + reading a couple of research papers + trying to deploy an LLM in production
Hello :) Today is Day 100! A quick summary of today: finished Vicki Boykis' 'What are embeddings?' read 2 papers Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Ying et al., 2018) TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation (El-Kishky et al., 2022) tried to deploy a PDF chat app using streamlit (spoiler: failed) My full notes from Vicki Boykis' What are embeddings? book The research papers below, I saw them in Vicki Boykis' vook and read them because they referenced GNNs. Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Ying et al., 2018) The paper talks about a a large-scale deep recommendation engine developed and deployed at Pinterest called PinSage which tackles the challenge of scaling deep neural networks for graph-structured data to web-scale recommendation tasks with huge amounts of users and items (pictures in pinterest's case). PinSage utilizes an efficient