Towards AI

APRIL 8, 2024

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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Natural Language Processing Clustering Algorithm AI 81

Mlearning.ai

APRIL 1, 2023

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.

Deep Learning 52

Deep Learning Deep Learning Natural Language Processing Clustering 52

phData

NOVEMBER 8, 2024

Partitioning and clustering features inherent to OTFs allow data to be stored in a manner that enhances query performance. 2013 - Apache Parquet and ORC These columnar storage formats were developed to optimize storage and speed within distributed storage and computing environments.

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

AWS Machine Learning Blog

MARCH 15, 2024

Solution overview We deploy FedML into multiple EKS clusters integrated with SageMaker for experiment tracking. EKS Blueprints helps compose complete EKS clusters that are fully bootstrapped with the operational software that is needed to deploy and operate workloads. He also holds an MBA from Colorado State University.

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AWS ML ML Machine Learning 117

Cambridge Intelligence

NOVEMBER 8, 2023

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

Clustering 64

Clustering Data Visualization Database Data Models 64

Explosion

FEBRUARY 18, 2015

Part-of-speech Tagger In 2013, I wrote a blog post describing how to write a good part of speech tagger. The tutorial also recommends the use of Brown cluster features, and case normalization features, as these make the model more robust and domain independent. spaCy’s tagger makes heavy use of these features.

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Algorithm Python Clustering 40

AWS Machine Learning Blog

MARCH 10, 2025

Founded in 2013, Octus, formerly Reorg, is the essential credit intelligence and data provider for the worlds leading buy side firms, investment banks, law firms and advisory firms. Our use of Amazon ECS with Fargate with auto scaling rules and controls gives us elastic scalability for our services during peak usage hours.

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Google Research AI blog

DECEMBER 8, 2022

A group of photons initially clustered on neighboring sites will evolve into a superposition of all possible paths each photon might have taken. Among all the possible configuration paths, the only possible scenario that survives is the configuration in which all photons remain clustered together in a bound state.

Clustering 52

Clustering AI AI 52

Flipboard

NOVEMBER 24, 2023

Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning. Greg has published research in the areas of operating systems, parallel computing, and distributed systems.

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Database AWS ETL SQL 157

PyImageSearch

OCTOBER 2, 2023

VAEs were introduced in 2013 by Diederik et al. By visualizing this space, colored by clothing type, as shown in Figure 9 , we can discern clusters, patterns, and potential correlations between different attributes. Similar class labels tend to form clusters, as observed with the Convolutional Autoencoder.

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Deep Learning Deep Learning Clustering Computer Science 117

Mlearning.ai

MARCH 9, 2023

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., 2012; Otsu, 1979; Long et al., Challenges in representation learning: A report on three machine learning contests. Neural Networks, 64, 59–63.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Explosion

FEBRUARY 28, 2017

For the similarity metric, I’ve been using a distance function taken from Chen (2013) , which he terms Cauchy Similarity : def ChenCauchy(length): '''Create a trainable similarity function, that will return the similarity and a callback to compute the backward pass given the gradient.

Algorithm 40

Algorithm Clustering 40

Hacker News

APRIL 18, 2024

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.

Natural Language Processing 116

Natural Language Processing Clustering Machine Learning Machine Learning 116

Flipboard

OCTOBER 16, 2023

Nagle’s brain implant, developed by the research consortium BrainGate , contained a “Utah” array, a cluster of 100 spiky electrodes that is surgically embedded into the brain. In 2006, Matthew Nagle, a man with spinal cord paralysis, received a brain implant that allowed him to control a computer cursor.

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AI AI Clustering Artificial Intelligence 181

Hacker News

MARCH 20, 2023

We tried several methods to reconstruct its original appearance: In 2013 we commissioned a picture by Greg O’Leary , a professional portrait artist. In 2012, with the permission of the police, Janette used a magnifying glass to find where several hairs came together in a cluster.

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Database Clustering AI AI 182

ML Review

MARCH 5, 2019

Well, actually, you’ll still have to wonder because right now it’s just k-mean cluster colour, but in the future you won’t). Within both embedding pages, the user can choose the number of embeddings to show, how many k-mean clusters to split these into, as well as which embedding type to show. References Harris, Z. Mikolov, T.,

AI 40

AI AI Clustering Machine Learning 40

DrivenData Labs

DECEMBER 7, 2023

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.

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Natural Language Processing Clustering Data Science Data Analysis 130

Flipboard

MARCH 28, 2025

As competition in AI intensifies, xAI has been ramping up its data center capacity to train more advanced models, and its supercomputer cluster in Memphis, called “Colossus,” is touted as the largest in the world. Musk in February made a $97.4 Reporting by Seher Dareen in Bengaluru; Editing by Pooja Desai and Sandra Maler

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Clustering Data Models Data Modeling AI 115

Pickl AI

FEBRUARY 23, 2025

Apache Hadoop Apache Hadoop is an open-source framework that allows for distributed storage and processing of large datasets across clusters of computers using simple programming models. Key Features : Scalability : Hadoop can handle petabytes of data by adding more nodes to the cluster. Statistics Kafka handles over 1.1

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Big Data Big Data Apache Hadoop Apache Kafka 52