Remove 2019 Remove Natural Language Processing Remove Supervised Learning
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Modern NLP: A Detailed Overview. Part 2: GPTs

Towards AI

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train.

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What Is a Transformer Model?

Hacker News

Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago.

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Foundation models: a guide

Snorkel AI

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy.

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AWS performs fine-tuning on a Large Language Model (LLM) to classify toxic speech for a large gaming company

AWS Machine Learning Blog

As an added inherent challenge, natural language processing (NLP) classifiers are historically known to be very costly to train and require a large set of vocabulary, known as a corpus , to produce accurate predictions. Improving Language Understanding by Generative Pre-Training” Devlin et al.,

AWS 86
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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. As AI adoption continues to accelerate, developing efficient mechanisms for digesting and learning from unstructured data becomes even more critical in the future. read HTML).

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Large language models: their history, capabilities and limitations

Snorkel AI

Data scientists and researchers train LLMs on enormous amounts of unstructured data through self-supervised learning. During the training process, the model accepts sequences of words with one or more words missing. The model then predicts the missing words (see “what is self-supervised learning?”

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Large language models: their history, capabilities and limitations

Snorkel AI

Data scientists and researchers train LLMs on enormous amounts of unstructured data through self-supervised learning. During the training process, the model accepts sequences of words with one or more words missing. The model then predicts the missing words (see “what is self-supervised learning?”