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cset.georgetown.edu | ||
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www.danieldemmel.me
Part two of the series Building applications using embeddings vector search and Large Language Models |
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austingil.com
Good things to understand when building AI applications: artificial neural networks, LLMs, parameters, embeddings, GPTs, and hallucinations. |
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blog.adnansiddiqi.me
Learn the basics of Large Language Models (LLMs) in this introduction to GenAI series. Discover how LLMs work, their architecture, and practical applications like customer support, content creation, and software development. |
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yang-song.net
This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... |
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