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Discipline Module 3 · Predictive Models

Natural Language Processing (Transformers + LLMs)

Workload
30h
Professor Prof. Dr. Filipe Nunes Ribeiro PhD in Computer Science from UFMG, with research experience at the Max Planck Institute and work in social computing and data mining.
Syllabus

Introduction to Natural Language Processing (NLP). Text preprocessing techniques. Foundations of semantic and vector text representation techniques. Notions of deep learning architectures for NLP: sequential models, Transformers, and large language models (LLMs).

Content
  • Introduction to Natural Language Processing (NLP)
  • Text preprocessing techniques
  • Foundations of semantic and vector text representation techniques
  • Notions of deep learning architectures for NLP: sequential models, Transformers, and large language models (LLMs)
Core Bibliography
  • Manning, C. D.; Schütze, H. Foundations of Statistical Natural Language Processing. MIT Press, 1999.
  • Rothman, D. Transformers for Natural Language Processing: Build Innovative Deep Neural Network Architectures for NLP with Python, PyTorch, TensorFlow, BERT, ROBERTa, and More. 1st ed., Packt Publishing, 2021.
  • Tunstall, L.; Von Werra, L.; Wolf, T. Natural Language Processing with Transformers, Revised Edition: Building Language Applications With Hugging Face. O'Reilly Media, 2022.
  • Raschka, S. Build a Large Language Model. Independently published, 2023.
  • Alammar, Jay, and Maarten Grootendorst. Hands-On Large Language Models. O'Reilly Media, Inc., 2024.
Complementary Bibliography
  • Jurafsky, D.; Martin, J. H. Speech and Language Processing. 3rd ed., Prentice Hall, 2021.
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