distance – Andrew Fairless, Ph.D.

What I Read: KL All You Need

By Andrew Fairless on August 21, 2024June 4, 2024

https://blog.alexalemi.com/kl-is-all-you-need.html KL is All You NeedAlexander A. Alemi2024-01-08 “…the core of essentially all modern machine learning methods is a single universal objective: Kullback-Leibler (KL) divergence minimization…. Understand KL, understand theContinue readingWhat I Read: KL All You Need

What I Read: Diffusion models, new theoretical perspective

By Andrew Fairless on April 8, 2024March 21, 2024

https://www.chenyang.co/diffusion.html Diffusion models from scratch, from a new theoretical perspectiveChenyang Yuan2023 “This tutorial aims to introduce diffusion models from an optimization perspective…”

What I Read: Estimate Token Importance in LLM Prompts

By Andrew Fairless on November 16, 2023October 5, 2023

https://www.watchful.io/blog/a-surprisingly-effective-way-to-estimate-token-importance-in-llm-prompts A Surprisingly Effective Way to Estimate Token Importance in LLM PromptsShayan Mohanty “Prompting is our primary means of instructing these AIs, yet the process of crafting effective prompts hasContinue readingWhat I Read: Estimate Token Importance in LLM Prompts

What I Read: To Understand Transformers, Focus on Attention

By Andrew Fairless on October 16, 2023September 4, 2023

https://drscotthawley.github.io/blog/posts/Transformers1-Attention.html To Understand Transformers, Focus on AttentionScott H. HawleyAugust 21, 2023 “To Understand Transformers, Focus on Attention”

What I Read: Multimodal, Embeddings

By Andrew Fairless on October 5, 2023September 4, 2023

https://app.twelvelabs.io/blog/multimodal-embeddings The Multimodal Evolution of Vector EmbeddingsJames LeAugust 9, 2023 “…companies collect and feed hundreds of millions of terabytes of multimodal data into deep learning models. As a result, embeddings…Continue readingWhat I Read: Multimodal, Embeddings

What I Read: Compare Two Ranked Lists

By Andrew Fairless on March 9, 2023January 30, 2023

https://towardsdatascience.com/how-to-objectively-compare-two-ranked-lists-in-python-b3d74e236f6a How to Objectively Compare Two Ranked Lists in PythonKrupesh RaikarJan 5 “Apart from the above movie viewing order disagreement, we are surrounded by examples of comparing lists!…. Ideally, aContinue readingWhat I Read: Compare Two Ranked Lists

What I Read: Dynamic Time Warping

By Andrew Fairless on July 6, 2022June 27, 2022

https://www.kdnuggets.com/2022/05/dynamic-time-warping-algorithm-time-series-explained.html Dynamic Time Warping Algorithm in Time Series, ExplainedBy Nagesh Singh ChauhanKDnuggetsMay 26, 2022 “In time series analysis, Dynamic Time Warping (DTW) is one of the algorithms for measuring theContinue readingWhat I Read: Dynamic Time Warping

What I Read: Understanding Levenshtein Distance

By Andrew Fairless on August 5, 2021July 12, 2021

https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0 Understanding the Levenshtein Distance Equation for BeginnersEthan NamFeb 26, 2019 “The Levenshtein distance is a number that tells you how different two strings are. The higher the number, theContinue readingWhat I Read: Understanding Levenshtein Distance

What I Read: Semantic Search

By Andrew Fairless on July 20, 2021July 2, 2021

https://www.pinecone.io/learn/semantic-search/ Semantic Search: Measuring Meaning From Jaccard to BertJames BriggsData Scientist “Similarity search is a complex topic and there are countless techniques for building effective search engines. In this article,Continue readingWhat I Read: Semantic Search

What I Read: Contrastive Representation Learning

By Andrew Fairless on July 1, 2021June 7, 2021

https://lilianweng.github.io/lil-log/2021/05/31/contrastive-representation-learning.html Contrastive Representation Learningby Lilian WengMay 31, 2021 “The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to eachContinue readingWhat I Read: Contrastive Representation Learning

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