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neo4j.com | ||
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andrevala.com
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| | | | | This week I was mostly focused on Microsoft Fabric, but I also read interesting articles on Computer Vision, Azure AI Document Intelligence, Embeddings and Vector Search. I'm also recommending a few GitHub repos around AI topics, two papers related to Large Language Models and more. Happy learning! | |
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www.onehouse.ai
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| | | | | Onehouse can host your vector embeddings, at low cost and with great performance. You can then move only needed vectors to a vector database for vector search use cases. | |
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bdtechtalks.com
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| | | | | Retrieval augmented generation (RAG) enables you to use custom documents with LLMs to improve their precision. | |
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coen.needell.org
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| | | In my last post on computer vision and memorability, I looked at an already existing model and started experimenting with variations on that architecture. The most successful attempts were those that use Residual Neural Networks. These are a type of deep neural network built to mimic specific visual structures in the brain. ResMem, one of the new models, uses a variation on ResNet in its architecture to leverage that optical identification power towards memorability estimation. M3M, another new model, ex... | ||
