Remove 2020 Remove ML Remove Natural Language Processing
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Groq sparks LPU vs GPU face-off

Dataconomy

GPUs: The versatile powerhouses Graphics Processing Units, or GPUs, have transcended their initial design purpose of rendering video game graphics to become key elements of Artificial Intelligence (AI) and Machine Learning (ML) efforts.

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In-person data science bootcamps are returning to Data Science Dojo

Data Science Dojo

” -DSD- Nothing can compare to Michael Jordan’s announcement in 1995 that he was returning to the NBA, but for Data Science Dojo (DSD), this comes close. In 2020, we had to move our in-person Data Science Bootcamp curriculum to an online format. Just because the bootcamp ends, doesn’t mean your education does.

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A Quick Recap of Natural Language Processing

Mlearning.ai

This ability to understand long-range dependencies helps transformers better understand the context of words and achieve superior performance in natural language processing tasks. billion parameters, and then GPT-3 arrived in 2020 with a whopping 175 billion parameters!! GPT-2 released with 1.5

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Machine Learning and Language (ML²) at CDS: Moving NLP Forward

NYU Center for Data Science

It’s a pivotal time in Natural Language Processing (NLP) research, marked by the emergence of large language models (LLMs) that are reshaping what it means to work with human language technologies. A Vision for ML² In the beginning, ML² was simply the hub for NLP research at NYU.

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Pioneering computer vision: Aleksandr Timashov, ML developer

Dataconomy

Aleksandr Timashov is an ML Engineer with over a decade of experience in AI and Machine Learning. This project dramatically improved the accessibility and utilisation of critical engineering information, enhancing operational efficiency and decision-making processes. Did the pandemic and isolation complicate your work?

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Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

Our pipeline belongs to the general ETL (extract, transform, and load) process family that combines data from multiple sources into a large, central repository. This can significantly shorten the time needed to deploy the Machine Learning (ML) pipeline to production. session.Session().region_name session.Session().region_name

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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.