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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. This architecture enables parallel computations and adeptly captures long-range dependencies, unlocking new possibilities for language models.

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A comprehensive guide to learning LLMs (Foundational Models)

Mlearning.ai

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

NLP 98
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Dude, Where’s My Neural Net? An Informal and Slightly Personal History

Lexalytics

And indeed we can see other machine learning topics arising to take their place, like “optimization” in the mid-’00s, with “deep learning” springing out of nowhere in 2012. And in 2012, Alex Krizhevsky, Ilya Sutskever and Geoffrey E. This is the sort of representation that is useful for natural language processing.

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Explosion in 2019: Our Year in Review

Explosion

The update fixed outstanding bugs on the tracker, gave the docs a huge makeover, improved both speed and accuracy, made installation significantly easier and faster, and added some exciting new features, like ULMFit/BERT/ELMo-style language model pretraining. ✨ Mar 20: A few days later, we upgraded Prodigy to v1.8

NLP 52
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Unsupervised Cross-lingual Representation Learning

Sebastian Ruder

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. In light of the success of pretrained language models, similar techniques have recently been applied to train unsupervised deep cross-lingual representations. 2019 ; Wu & Dredze, 2019 ). 2019 ; Wu et al., 2019 , Anonymous et al.,

BERT 52
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Build high-performance ML models using PyTorch 2.0 on AWS – Part 1

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. This leads to improved performance compared to vanilla BERT. With the recent PyTorch 2.0 torch.compile + bf16 + fused AdamW.

ML 72