denocris/MHPC-Natural-Language-Processing-Lectures
This is the second part of the Deep Learning Course for the Master in High-Performance Computing (SISSA/ICTP).)
This collection of lectures helps students and researchers understand and implement modern Natural Language Processing (NLP) techniques, particularly those powered by transformer-based models. It takes raw text data and, through practical examples and code, demonstrates how to process it for tasks like named entity recognition, part-of-speech tagging, and word sense disambiguation. The primary users are individuals pursuing a Master's in High-Performance Computing or anyone looking to deepen their expertise in advanced NLP.
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Use this if you are a graduate student or researcher looking for a structured deep dive into transformer models and their application in NLP, with practical PyTorch examples.
Not ideal if you are a business user seeking a no-code solution for NLP tasks or a developer looking for a plug-and-play library without the need for foundational understanding.
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