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The Illustrated Word2Vec (2019)

Hacker News

You can find it in the turning of the seasons, in the way sand trails along a ridge, in the branch clusters of the creosote bush or the pattern of its leaves. Word2vec is a method to efficiently create word embeddings and has been around since 2013. Yet, it is possible to see peril in the finding of ultimate perfection.

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Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

AWS Machine Learning Blog

In this post, you’ll see an example of performing drift detection on embedding vectors using a clustering technique with large language models (LLMS) deployed from Amazon SageMaker JumpStart. Then we use K-Means to identify a set of cluster centers. A visual representation of the silhouette score can be seen in the following figure.

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Financial Market Challenges and ML-Supported Asset Allocation

ODSC - Open Data Science

For example, rising interest rates and falling equities already in 2013 and again in 2020 and 2022 led to drawdowns of risk parity schemes. In 2023-Q1, we even saw failing banks like SVB simply because of investments in “safe” treasury bonds.

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Six keys to achieving advanced container monitoring

IBM Journey to AI blog

Containers have increased in popularity and adoption ever since the release of Docker in 2013, an open-source platform for building, deploying and managing containerized applications. However, monitoring remains critical, so that organizations have a view into each Kubernetes cluster.

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Deep Learning for NLP: Word2Vec, Doc2Vec, and Top2Vec Demystified

Mlearning.ai

It was first introduced in 2013 by a team of researchers at Google led by Tomas Mikolov. Image taken from Efficient Estimation of Word Representation in Vector Space Top2Vec Top2Vec is an unsupervised machine-learning model designed for topic modelling and document clustering. To achieve this, Top2Vec utilizes the doc2vec model.

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Tuning Word2Vec with Bayesian Optimization: Applied to Music Recommendations

Towards AI

Developed by researchers at Google in 2013 [1], Word2Vec leverages neural networks to learn dense vector representations of words, capturing their semantic and contextual relationships. Understanding Word2Vec Word2Vec is a pioneering natural language processing (NLP) technique that revolutionized the way we represent words in vector space.

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Understanding earthquakes: what map visualizations teach us

Cambridge Intelligence

Earthquake data as a geospatial visualization A map view reveals what we’d expect, that the largest clusters of earthquakes occur where tectonic plates meet. Additional investigation reveals that this was the 2013 Russian Okhotsk Sea earthquake. Let’s focus on the major earthquakes between 2010-2011, including the 9.1