Remove 2010 Remove Big Data Remove Deep Learning
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How does Facebook use Big Data?

Pickl AI

But deploying conventional methods to extract insight from this data is not feasible. Here comes the role of Big Data. The Symbiotic Relationship Between Facebook and Big Data Facebook has been leveraging Big Data technology to extract meaningful insights. It’s actually Big Data technologies.

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How artificial intelligence went from science fiction to science itself?

Dataconomy

Nonetheless, starting from around 2010, there has been a renewed surge of interest in the field. This can be attributed primarily to remarkable advancements in computer processing power and the availability of vast amounts of data. Deep learning emerged as a highly promising machine learning technology for various applications.

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How AI Is Paving the Way for Improved Surveillance and Cybersecurity

Dataversity

Cybersecurity is increasingly leaning towards artificial intelligence (AI) to help mitigate threats because of the innate ability AI has to turn big data into actionable insights. Rightly so, because the threat to data security is real, and across all industries.

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

He focuses on Deep learning including NLP and Computer Vision domains. 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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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

SageMaker Canvas supports a number of use cases, including time-series forecasting , which empowers businesses to forecast future demand, sales, resource requirements, and other time-series data accurately. As a Data Engineer he was involved in applying AI/ML to fraud detection and office automation.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Thirdly, the presence of GPUs enabled the labeled data to be processed. Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. In order to train transformer models on internet-scale data, huge quantities of PBAs were needed.

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