Remove 2008 Remove Algorithm Remove Natural Language Processing
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Getting Started with AI

Towards AI

In ML, there are a variety of algorithms that can help solve problems. There is often confusion between the terms artificial intelligence and machine learning, which is discussed in The AI Process. There is often confusion between the terms artificial intelligence and machine learning, which is discussed in The AI Process.

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Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

We also demonstrate how you can engineer prompts for Flan-T5 models to perform various natural language processing (NLP) tasks. Task Prompt (template in bold) Model output Summarization Briefly summarize this paragraph: Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents.

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Joshua Walker: Using Data to Improve the Legal System

DataRobot

Natural language processing used to be a dirty word because it didn’t really work. Then we have algorithms, and algorithms are tools for resolving disputes. That is what led Joshua to found Lex Machina in 2008. That’s something we can grapple with, and that doesn’t terrify people. Is this a cat or a dog?’

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Customizing coding companions for organizations

AWS Machine Learning Blog

This retrieval can happen using different algorithms. Her research interests lie in Natural Language Processing, AI4Code and generative AI. His research interests lie in the area of AI4Code and Natural Language Processing. He received his PhD in Computer Science from Purdue University in 2008.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 1

AWS Machine Learning Blog

Because ML algorithms are often not adequate in protecting the privacy of patient-level data, there is a growing interest among HCLS partners and customers to use privacy-preserving mechanisms and infrastructure for managing and analyzing large-scale, distributed, and sensitive data. [1].

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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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