AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). If you have a large dataset, the SageMaker KNN algorithm may provide you with an effective semantic search.

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Algorithm Machine Learning Machine Learning Natural Language Processing 86

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. The following figure illustrates the Neuron software stack.

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Flipboard

JULY 10, 2023

By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ).

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Pickl AI

DECEMBER 2, 2024

According to a report by Statista, the global data sphere is expected to reach 180 zettabytes by 2025 , a significant increase from 33 zettabytes in 2018. Processing frameworks like Hadoop enable efficient data analysis across clusters. Introduction In today’s digital age, the volume of data generated is staggering.

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Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

According to a report by Statista, the global data sphere is expected to reach 180 zettabytes by 2025 , a significant increase from 33 zettabytes in 2018. Processing frameworks like Hadoop enable efficient data analysis across clusters. Introduction In today’s digital age, the volume of data generated is staggering.

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Big Data Big Data Data Lakes Apache Hadoop 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle). Each season consists of around 17,000 plays.

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Mlearning.ai

JUNE 8, 2023

Use algorithm to determine closeness/similarity of points. Clustering  — we can cluster our sentences, useful for topic modeling. SentenceBERT: Currently, the leader among the pack, SentenceBERT was introduced in 2018 and immediately took the pole position for Sentence Embeddings. The new model offers: 90%-99.8%

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Machine Learning Machine Learning Clustering Database 52

DrivenData Labs

FEBRUARY 3, 2025

Dueweke and Bridges, 2018 ) To better guide suicide prevention, we must first understand the series of events that victims go through in the days, weeks, or even months prior to death. Then we leveraged the benefits of NLP algorithms (e.g., Patient stories are rarely documented as part of their medical chart ( Rimkeviciene et al.,

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Heartbeat

AUGUST 23, 2023

Consider a scenario where legal practitioners are armed with clever algorithms capable of analyzing, comprehending, and extracting key insights from massive collections of legal papers. Algorithms can automatically detect and extract key items. But what if there was a technique to quickly and accurately solve this language puzzle?

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PyImageSearch

JULY 3, 2023

Figure 1: Netflix Recommendation System (source: “Netflix Film Recommendation Algorithm,” Pinterest ). Netflix recommendations are not just one algorithm but a collection of various state-of-the-art algorithms that serve different purposes to create the complete Netflix experience. Each row has a title (e.g.,

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Deep Learning Deep Learning Algorithm Machine Learning 52

Hacker News

JANUARY 9, 2024

Sometimes it’s a story of creating a superalgorithm that encapsulates decades of algorithmic development. Talking of speedups, another example—made possible by new algorithms operating on multithreaded CPUs—concerns polynomials. In addition, a new algorithm in Version 14.0 but with things like clustering). there are 6602.

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Mlearning.ai

JANUARY 17, 2024

Traditional AI can recognize, classify, and cluster, but not generate the data it is trained on. The foundations for today’s generative language applications were elaborated in the 1990s ( Hochreiter , Schmidhuber ), and the whole field took off around 2018 ( Radford , Devlin , et al.). Let’s play the comparison game.

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AWS Machine Learning Blog

JANUARY 13, 2023

The eICU data is ideal for developing ML algorithms, decision support tools, and advancing clinical research. FedML supports several out-of-the-box deep learning algorithms for various data types, such as tabular, text, image, graphs, and Internet of Things (IoT) data. 2018): 1-13. [2] Define the model. Reference. [1]

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Google Research AI blog

FEBRUARY 28, 2023

For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program , where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group.

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ML Review

MARCH 5, 2019

Word embeddings Visualisation of word embeddings in AI Distillery Word2vec is a popular algorithm used to generate word representations (aka embeddings) for words in a vector space. Then, the algorithm proceeds with the following word as the new centre word, i.e. “learning”, sets up the new context, and repeats the same procedure.

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AWS Machine Learning Blog

JANUARY 17, 2024

nnIn 1996, Moret founded the ACM Journal of Experimental Algorithmics, and he remained editor in chief of the journal until 2003. About the Authors Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms.

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. 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.

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