https://gestalt.ink/gaussians Understanding Gaussians “The Gaussian distribution, or normal distribution is a key subject in statistics, machine learning, physics, and pretty much any other field that deals with data and probability.”
What I Read: cosine similarity
https://tomhazledine.com/cosine-similarity-alternatives Alternatives to cosine similarityTom Hazledine9/20/24 8:00 PM “Cosine similarity is the recommended way to compare vectors, but what other distance functions are there? And are any of them better?”
What I Read: Transformers Inference Optimization
https://astralord.github.io/posts/transformer-inference-optimization-toolset Transformers Inference Optimization ToolsetAleksandr SamarinOct 1, 2024 “Large Language Models are pushing the boundaries of artificial intelligence, but their immense size poses significant computational challenges. As these models grow,
What I Read: embedding models
https://unstructured.io/blog/understanding-embedding-models-make-an-informed-choice-for-your-rag Understanding embedding models: make an informed choice for your RAGMaria KhalusovaAug 13, 2024 “How do you choose a suitable embedding model for your RAG application?”
What I Watch: How LLMs store facts
How might LLMs store facts | Chapter 7, Deep Learning3Blue1BrownAug 31, 2024 “Unpacking the multilayer perceptrons in a transformer, and how they may store facts”
What I Watch: compare high dimensional vectors
A new way to compare high dimensional vectorsTunadorableAug 26, 2024 “Surpassing Cosine Similarity for Multidimensional Comparisons: Dimension Insensitive Euclidean Metric (DIEM)”