AWS Machine Learning Blog

NOVEMBER 9, 2023

Her research interests lie in Natural Language Processing, AI4Code and generative AI. Xiaofei has been serving as the science manager for several services including Kendra, Contact Lens, and most recently CodeWhisperer and CodeGuru Security. He received his PhD in Computer Science from Purdue University in 2008.

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AWS Machine Learning Blog

JANUARY 13, 2023

At the application level, such as computer vision, natural language processing, and data mining, data scientists and engineers only need to write the model, data, and trainer in the same way as a standalone program and then pass it to the FedMLRunner object to complete all the processes, as shown in the following code.

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DataRobot

AUGUST 3, 2021

Natural language processing used to be a dirty word because it didn’t really work. That alone makes him one of the main drivers of computer science.”. That is what led Joshua to found Lex Machina in 2008. That’s something we can grapple with, and that doesn’t terrify people.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

” Advances in neural information processing systems 32 (2019). He is broadly interested in Deep Learning and Natural Language Processing. He has a degree in Mathematics and Computer Science from the University of Illinois at Urbana Champaign. Van der Maaten, Laurens, and Geoffrey Hinton.

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AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more.

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AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In computer science, a number can be represented with different levels of precision, such as double precision (FP64), single precision (FP32), and half-precision (FP16). The benchmark used is the RoBERTa-Base, a popular model used in natural language processing (NLP) applications, that uses the transformer architecture.

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