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www.danieldemmel.me | ||
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www.shakudo.io
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| | | | | A step-by-step guide to chat with your PDFs and extract information using open-source LLMs on Shakudo. | |
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michael-lewis.com
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| | | | | An introduction to vector search (aka semantic search), and Retrieval Augmented Generation (RAG). | |
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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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blog.fastforwardlabs.com
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| | | Graph Neural Networks (GNNs) are neural networks that take graphs as inputs. These models operate on the relational information in data to produce insights not possible in other neural network architectures and algorithms. While there is much excitement in the deep learning community around GNNs, in industry circles, this is sometimes less so. So, I'll review a few exciting applications empowered by GNNs. Overview of Graphs and GNNs A graph (sometimes called a network) is a data structure that highlights the relationships between components in the data. | ||
