Remove 2009 Remove ML 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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Top 5 Generative AI Integration Companies to drive Customer Support in 2023

Chatbots Life

10CLOUDS Year Founded : 2009 HQ : Warsaw, Poland Team Size : 51–200 employees Clients : TrustStamp (Identity verification), Emergent Tech (G-Coin), AlephZero (Blockchain), Tapeke (BitCoin Software Development), Tagasauris (Crowdsourcing Software Development), CallerSmart. Elite Service Delivery partner of NVIDIA.

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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. They can process various types of input data, including tabular, image, and text. 2 3175 3294 0.94

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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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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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How Active Learning Can Improve Your Computer Vision Pipeline

DagsHub

  Provides a Python API for customization and integration with existing ML pipelines. For each type, we will have an overview, key characteristics, applications, and advantages so that we will have a structured form of understanding.    Overview of the types of active learning | Source : Settles, B.

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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. You can then choose Train to start the training job on a SageMaker ML instance.

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