Mlearning.ai

APRIL 8, 2023

With the rise of deep learning (deep learning means multiple levels of neural networks) and neural networks, models such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) began to be used in NLP. 2018) “ Language models are few-shot learners ” by Brown et al.

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field. Top-1 accuracy on ImageNet-1K, 53.9

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Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Snorkel AI

MARCH 1, 2023

BERT BERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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BERT Natural Language Processing Large Language Models Neural Network 83

DataRobot Blog

MARCH 9, 2022

The company was acquired by DataRobot in 2021. These embeddings are sometimes trained jointly with the model, but usually additional accuracy can be attained by using pre-trained embeddings such as Word2Vec, GloVe, BERT, or FastText. This article was originally published at Algorithimia’s website. and discern what’s behind it.

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Sebastian Ruder

JANUARY 19, 2021

A plethora of language-specific BERT models have been trained for languages beyond English such as AraBERT ( Antoun et al.,  While Transformers have achieved large success in NLP, they were—up until recently—less successful in computer vision where convolutional neural networks (CNNs) still reigned supreme.

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