Remove 2013 Remove Clustering Remove Natural Language Processing
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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

Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with language in a numeric form. 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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Tuning Word2Vec with Bayesian Optimization: Applied to Music Recommendations

Towards AI

While numerous techniques have been explored, methods harnessing natural language processing (NLP) have demonstrated strong performance. 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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Deep Learning for NLP: Word2Vec, Doc2Vec, and Top2Vec Demystified

Mlearning.ai

NLP A Comprehensive Guide to Word2Vec, Doc2Vec, and Top2Vec for Natural Language Processing In recent years, the field of natural language processing (NLP) has seen tremendous growth, and one of the most significant developments has been the advent of word embedding techniques.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

The intersection of AI and financial analysis presents a compelling opportunity to transform how investment professionals access and use credit intelligence, leading to more efficient decision-making processes and better risk management outcomes. Amazon Bedrock Knowledge Bases provides efficient access to the document repository.

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Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Mlearning.ai

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2013; Goodfellow et al., Generative adversarial networks-based adversarial training for natural language processing. 2012; Otsu, 1979; Long et al.,

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Meet the winners of the Unsupervised Wisdom Challenge!

DrivenData Labs

Solvers submitted a wide range of methodologies to this end, including using open-source and third party LLMs (GPT, LLaMA), clustering (DBSCAN, K-Means), dimensionality reduction (PCA), topic modeling (LDA, BERT), sentence transformers, semantic search, named entity recognition, and more. and DistilBERT.