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garrit.xyz | ||
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blog.codefarm.me
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| | | | | [AI summary] This post explores the relationship between dimensionality and embedding models in machine learning, explaining their roles in data representation and how to optimize them for text-based knowledge base systems using Milvus. | |
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tanelpoder.com
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| | | | | Disclaimer: I'm not an ML expert and not even a serious ML specialist (yet?), so feel free to let me know if I'm wrong! It seems to me that we have hit a bit of an "on-premises" vs. "on-premise" situation in the ML/AI and vector search terminology space. The majority of product announcements, blog articles and even some papers I've read use the term vector embeddings to describe embeddings, but embeddings already are vectors themselves! - Linux, Oracle, SQL performance tuning and troubleshooting training & writing. | |
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www.ethanrosenthal.com
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| | | | | Spoiler alert: the answer is maybe! Although, my inclusion of the word "actually" betrays my bias. Vector databases are having their day right now. Three different vector DB companies have raised money on valuations up to $700 million (paywall link). Surprisingly, their rise in popularity is not for their "original" purpose in recommendation systems, but rather as an auxillary tool for Large Language Models (LLMs). Many online examples of combining embeddings with LLMs will show you how they store the em... | |
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codeincomplete.com
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| | | Personal Website for Jake Gordon | ||
