vector – Page 2 – Andrew Fairless, Ph.D.

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: speed up Numba and NumPy

By Andrew Fairless on August 1, 2023June 15, 2023

https://pythonspeed.com/articles/speeding-up-numba/ Understanding CPUs can help speed up Numba and NumPy codeby Itamar Turner-TrauringLast updated 08 Jun 2023, originally created 07 Jun 2023 “In practice, your initial Numba code may beContinue readingWhat I Read: speed up Numba and NumPy

What I Read: Computation, Artificial Intelligence

By Andrew Fairless on June 20, 2023May 1, 2023

https://www.quantamagazine.org/a-new-approach-to-computation-reimagines-artificial-intelligence-20230413/ A New Approach to Computation Reimagines Artificial IntelligenceAnil AnanthaswamyApril 13, 2023 “By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficientlyContinue readingWhat I Read: Computation, Artificial Intelligence

What I Read: Bayesian Geometry

By Andrew Fairless on February 21, 2022January 20, 2022

https://towardsdatascience.com/on-bayesian-geometry-7755abf9f4d2 On Bayesian GeometryGeometric interpretation of probability distributionsMichael Larionov, PhD “Bayesian inference is based on the fact that we often don’t know the underlying distribution of data, so we needContinue readingWhat I Read: Bayesian Geometry

What I Read: Dense Vectors

By Andrew Fairless on December 14, 2021November 16, 2021

https://www.pinecone.io/learn/dense-vector-embeddings-nlp/ Dense Vectors: Capturing Meaning with CodeJames BriggsData Scientist “But since the days of word2vec, developments in representing language have advanced at ludicrous speeds. This article will explore why weContinue readingWhat I Read: Dense Vectors

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

What I Read: Basis and Change of Basis

By Andrew Fairless on June 30, 2021June 7, 2021

https://hadrienj.github.io/posts/Essential-Math-for-Data-Science-Change-of-Basis/ Essential Math for Data Science: Basis and Change of Basis01-02-2021Hadrien Jean “One way to understand eigendecomposition is to consider it as a change of basis…. You’ll see that anyContinue readingWhat I Read: Basis and Change of Basis

What I Read: What is a Vector Database?

By Andrew Fairless on June 4, 2021May 14, 2021

https://www.pinecone.io/learn/vector-database/ What is a Vector Database?Rajat TripathiML Solutions Engineer “The meteoric rise in Machine Learning in the last few years has led to increasing use of vector embeddings…. The uniqueContinue readingWhat I Read: What is a Vector Database?

What I Read: ScaNN, Efficient Vector Similarity Search

By Andrew Fairless on January 14, 2021December 23, 2020

https://www.googblogs.com/announcing-scann-efficient-vector-similarity-search/ Announcing ScaNN: Efficient Vector Similarity SearchPosted by Philip Sun, Software Engineer, Google Research “Based on this intuition, we more heavily penalize quantization error that is parallel to the originalContinue readingWhat I Read: ScaNN, Efficient Vector Similarity Search

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