https://adamkarvonen.github.io/machine_learning/2024/06/11/sae-intuitions.html An Intuitive Explanation of Sparse Autoencoders for LLM InterpretabilityAdam KarvonenJun 11, 2024 “Sparse Autoencoders (SAEs) have recently become popular for interpretability of machine learning models…”
What I Read: LLMs, School Math
https://towardsdatascience.com/understanding-llms-from-scratch-using-middle-school-math-e602d27ec876?gi=551c5bfd7f21 Understanding LLMs from Scratch Using Middle School MathRohit PatelOct 19, 2024 “In this article, we talk about how Large Language Models (LLMs) work, from scratch — assuming only that
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 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)”
What I Read: Classifying pdfs
https://snats.xyz/pages/articles/classifying_a_bunch_of_pdfs.html Classifying all of the pdfs on the internetSantiago Pedroza2024-08-18 “How would you classify all the pdfs in the internet? Well, that is what I tried doing this time.”
What I Read: Illustrated AlphaFold
https://elanapearl.github.io/blog/2024/the-illustrated-alphafold The Illustrated AlphaFoldElana Simon, Jake Silberg “A visual walkthrough of the AlphaFold3 architecture…”
What I Read: AI Engineers, Search
https://softwaredoug.com/blog/2024/06/25/what-ai-engineers-need-to-know-search What AI Engineers Should Know about SearchDoug TurnbullJune 25th, 2024 “Things AI Engineers Should Know about Search”