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 michael-lewis.com
Vector search and Retrieval Augmented Generation ยท Michael I Lewis
| | www.danieldemmel.me
Understanding embeddings and how to use them for semantic search
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| | Part one of the series Building applications using embeddings vector search and Large Language Models
| | bdtechtalks.com
No-code retrieval augmented generation (RAG) with LlamaIndex and ChatGPT - TechTalks
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| | Retrieval augmented generation (RAG) enables you to use custom documents with LLMs to improve their precision.
| | wandb.ai
What is Retrieval Augmented Generation (RAG)? - Weights & Biases
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| | [AI summary] This article explains Retrieval Augmented Generation (RAG), a technique that enhances AI models by integrating real-time data retrieval with generative models to improve accuracy, reduce hallucinations, and adaptability in dynamic fields like healthcare, finance, and customer support.
| | blog.otoro.net
Neural Network Evolution Playground with Backprop NEAT | ???
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| [AI summary] This article describes a project that combines genetic algorithms, NEAT (NeuroEvolution of Augmenting Topologies), and backpropagation to evolve neural networks for classification tasks. The key components include: 1) Using NEAT to evolve neural networks with various activation functions, 2) Applying backpropagation to optimize the weights of these networks, and 3) Visualizing the results of the evolved networks on different datasets (e.g., XOR, two circles, spiral). The project also includes a web-based demo where users can interact with the system, adjust parameters, and observe the evolution process. The author explores how the genetic algorithm can discover useful features (like squaring inputs) without human intervention, and discusses the ...