Remove 2009 Remove Algorithm Remove Natural Language Processing
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Introducing NYU Center for Data Science Research Groups

NYU Center for Data Science

And how can we best use insights from natural intelligence to develop new, more powerful machine intelligence technologies that more fruitfully interact with us?” The group works on machine learning in a broad range of applications, predominately in computer perception, natural language understanding, robotics, and healthcare.

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Amazon SageMaker built-in LightGBM now offers distributed training using Dask

AWS Machine Learning Blog

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. He was a recipient of the NSF Faculty Early Career Development Award in 2009. He founded StylingAI Inc.,

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Top recommended AI companies in Vietnam to collaborate in 2024

Dataconomy

The company is renowned for its deep understanding of machine learning and natural language processing technologies, providing practical AI solutions tailored to businesses’ unique needs. Their team of AI experts excels in creating algorithms for deep learning, predictive analytics, and automation.

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

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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Fine-tune Llama 2 for text generation on Amazon SageMaker JumpStart

AWS Machine Learning Blog

You can easily try out these models and use them with SageMaker JumpStart, which is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. Fine-tuning technique Language models such as Llama are more than 10 GB or even 100 GB in size. Default is 5.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

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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