Hacker News

MARCH 25, 2022

Attention Net didn’t sound very exciting,” said Vaswani, who started working with neural nets in 2011.Jakob For example, researchers from the Rostlab at the Technical University of Munich, which helped pioneer work at the intersection of AI and biology, used natural-language processing to understand proteins.

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AWS Machine Learning Blog

SEPTEMBER 9, 2024

As LLMs have grown larger, their performance on a wide range of natural language processing tasks has also improved significantly, but the increased size of LLMs has led to significant computational and resource challenges. degree in Computer Science in 2011 from the University of Lille 1. He holds a M.E.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform. Our pipeline belongs to the general ETL (extract, transform, and load) process family that combines data from multiple sources into a large, central repository.

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AWS Machine Learning Blog

OCTOBER 23, 2023

Founded in 2011, Talent.com is one of the world’s largest sources of employment. With over 30 million jobs listed in more than 75 countries, Talent.com serves jobs across many languages, industries, and distribution channels.

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Dataconomy

JUNE 15, 2023

This breakthrough enabled faster and more powerful computations, propelling AI research forward One notable public achievement during this time was IBM’s AI system, Watson, defeating two champions on the game show Jeopardy in 2011. This demonstrated the astounding potential of machines to learn and differentiate between various objects.

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IBM Journey to AI blog

OCTOBER 12, 2023

In other words, traditional machine learning models need human intervention to process new information and perform any new task that falls outside their initial training. For example, Apple made Siri a feature of its iOS in 2011. This early version of Siri was trained to understand a set of highly specific statements and requests.

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Dataconomy

AUGUST 23, 2023

Decoding the complexity of human language Perhaps one of the most awe-inspiring innovations of artificial intelligence is the development of Natural Language Processing (NLP) capabilities. This 2011 released game, which is famous for its modding community, has been mentioned again with a sensational mod in recent months.

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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). They’re mostly trained on general domain corpora, making them less effective on domain-specific tasks.

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Algorithm Machine Learning Machine Learning Natural Language Processing 101

DagsHub

JULY 25, 2024

Libraries and Extensions: Includes torchvision for image processing, touchaudio for audio processing, and torchtext for NLP. Notable Use Cases PyTorch is extensively used in natural language processing (NLP), including applications like sentiment analysis, machine translation, and text generation. In 2011, H2O.ai

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Alation

FEBRUARY 20, 2020

While this requires technology – AI, machine learning, log parsing, natural language processing,metadata management, this technology must be surfaced in a form accessible to business users – the data catalog. Here’s why your organization should catch the Wave. Subscribe to Alation's Blog.

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Machine Learning Machine Learning Natural Language Processing Analytics 52

Dataconomy

NOVEMBER 29, 2023

That simplicity is its biggest selling point – beginners can get well-written paraphrased articles by simply copying and pasting existing copy into the tool. Fast bulk content generation is its major selling point, with the tool being capable of spinning one article into 500 unique pieces in less than a minute.

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AI AI Natural Language Processing Artificial Intelligence 103

Explosion

FEBRUARY 12, 2017

The Quora dataset is an example of an important type of Natural Language Processing problem: text-pair classification. People have been using context windows as features since at least Collobert and Weston (2011) , and likely much before. In updated experiments the Maxout Window Encoding helps as expected.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading! Online] arXiv: 1710.01288.

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Explosion

DECEMBER 17, 2013

Natural languages introduce many unexpected ambiguities, which our world-knowledge immediately filters out. ACL 2011 The dynamic oracle training method was first described here: A Dynamic Oracle for Arc-Eager Dependency Parsing. Syntactic Processing Using the Generalized Perceptron and Beam Search.

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Python Algorithm Natural Language Processing 45

Explosion

MARCH 17, 2019

of the spaCy Natural Language Processing library includes a huge number of features, improvements and bug fixes. spaCy is an open-source library for industrial-strength natural language processing in Python. Version 2.1 Prodigy is a fully scriptable annotation tool that complements spaCy extremely well.

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AWS Machine Learning Blog

JULY 20, 2023

They have been proven to be efficient in diverse applications and learning settings such as cybersecurity [1] and fraud detection, remote sensing, predicting best next steps in financial decision-making, medical diagnosis, and even computer vision and natural language processing (NLP) tasks. References [1] Raj Kumar, P.

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AWS Machine Learning Blog

FEBRUARY 19, 2025

Physical damage coverage for vehicles with an ISO symbol of more than 20 for model year 2010 and earlier or ISO symbol 41 for model year 2011 and later. Liability coverage for vehicles with an ISO symbol of more than 25 for vehicles with model year 2010 and earlier or ISO symbol 59 for model year 2011 and later.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

text generation model on domain-specific datasets, enabling it to generate relevant text and tackle various natural language processing (NLP) tasks within a particular domain using few-shot prompting. This fine-tuning process involves providing the model with a dataset specific to the target domain.

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