GT NLP Bootcamp: Natural Language Processing & Large Language Model

Table of Contents

Logistics

Learning Objective

This course offers a comprehensive coverage of the latest machine learning techniques for Natural Language Processing (NLP), with a particular emphasis on representation learning and large language models (LLMs) that have recently gained significant prominence. Participants will be introduced to contemporary NLP challenges, along with key solutions and their respective strengths and weaknesses.

Course Objectives:

  • Develop a thorough understanding of NLP problems and the ability to select appropriate models for representation learning.
  • Cultivate the skills necessary to devise innovative solutions to address ongoing research challenges in the field of NLP.

Target Audience: This bootcamp is designed to benefit individuals interested in solving real-world text data problems using NLP and LLMs, as well as those pursuing cutting-edge research in text mining, NLP, and artificial intelligence.

Schedule

Date Time Topic   Recording
Day 1 9am - 9:50am Overview and Text Representation Chao recording
  10am - 10:50am Word Embedding Yinghao recording
  11am - 11:50am Practice Session of Word Embedding Yuchen  
  12pm - 1pm Lunch    
  1pm - 1:50pm Neural Networks (MLP, backpropagation) Chao recording
  2pm - 2:50pm CNN & RNN Yuchen recording
  3pm - 3:50pm Practice Session of Neural Networks Yinghao recording
Day 2 9am - 9:50am Transformer & Self Attention Chao recording
  10am - 10:50am Language Model Pre-training Yinghao recording
  11am - 11:50am Practice Session for Transformers Yuchen recording
  12pm - 1pm Lunch    
  1pm - 1:50pm LLM Instruction Fine-Tuning Chao recording
  2pm - 2:50pm LLM Alignment Yuchen recording
  3pm - 3:50pm Practice Session of LLM Fine-tuning Yinghao recording
  3:50pm - 4pm Wrapup    

More Resources

Other resources, such as deep learning toolboxes and datasets, will be provided throughout the course.