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GitHub - nlp-with-transformers/notebooks: Jupyter notebooks for the Natural Language Processing with Transformers book
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Artificial Intelligence (AI) advancements have enabled the development of Large Language Models (LLMs) that can perform a variety of tasks with remarkable semantic understanding and accuracy. ChatGPT is one such LLM that has gained significant attention due to its impressive capabilities for assisting in various knowledge-intensive tasks. Due to the knowledge-intensive nature of engineering secure
Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing models
Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing models Teaching computers to understand how humans write and speak, known as natural language processing (NLP), is one of the oldest challenges in AI research. There has been a marked change in approach over the past two years, however. Where research once focused on developing specific frameworks
Natural Language Processing (NLP): Don’t Reinvent the Wheel
(Image by Author)IntroductionNatural language processing (NLP) is an intimidating name for an intimidating field. Generating useful insight from unstructured text is hard, and there are countless techniques and algorithms out there, each with their own use-cases and complexities. As a developer with minimal NLP exposure, it can be difficult to know which methods to use, and how to implement them.
How Thomson Reuters accelerated research and development of natural language processing solutions with Amazon SageMaker | Amazon Web Services
AWS Machine Learning Blog How Thomson Reuters accelerated research and development of natural language processing solutions with Amazon SageMaker This post is co-written by John Duprey and Filippo Pompili from Thomson Reuters. Thomson Reuters (TR) is one of the world’s most trusted providers of answers, helping professionals make confident decisions and run better businesses. Teams of experts from
GitHub - yohasebe/ruby-spacy: A wrapper module for using spaCy natural language processing library from the Ruby programming language via PyCall
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A thorough guide for programmers working with Japanese text, covering fundamental issues like tokenization and recent research topics like generating natural language texts. Working examples are accompanied by extensive reference to allow problem solving even without a background in Japanese or Machine Learning.
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(PDF) Natural Language Processing with Python
Introduction to Japanese Natural Language Processing
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