Advanced Transformers
Course Overview:
This course delves into the complexities of Advanced Transformers, a revolutionary architecture pushing the boundaries of Natural Language Processing (NLP) and impacting Supply Chain Management (SCM) by enabling the analysis and understanding of vast amounts of textual data. You'll explore advanced transformer techniques and their applications in extracting valuable insights from diverse text sources within your supply chains.
Learning Objectives:
Explore advanced transformer architectures like BERT, GPT-3, and their capabilities for various NLP tasks.
Understand the concept of self-attention and its role in advanced transformer models.
Gain insights into fine-tuning pre-trained transformers for specific SCM applications (e.g., sentiment analysis of customer reviews, topic modeling of social media data).
Identify ethical considerations surrounding the use of large language models (LLMs) like GPT-3 in SCM.
Analyze the potential of advanced transformers for tasks beyond text analysis (e.g., code generation for automating tasks).
Course Highlights:
1. Unveiling Advanced Transformers
Beyond the Basics: Exploring advanced transformer architectures like BERT (Bidirectional Encoder Representations from Transformers).
Demystifying Self-Attention: Understanding how self-attention mechanisms enhance the power of transformers for NLP tasks.
Hands-on Exercises (Optional): Utilizing online tools or libraries (TensorFlow Hub) to explore pre-trained transformer models and their functionalities.
Case Studies: Exploring applications of BERT in sentiment analysis of customer reviews for product quality improvement in SCM.
Deep dive into other advanced architectures like GPT-3 and their capabilities (optional).
2. Transformers for Advanced SCM Applications
Fine-tuning Pre-trained Transformers: Tailoring powerful models for specific SCM tasks (e.g., demand forecasting from text data).
Understanding transfer learning and fine-tuning techniques for leveraging pre-trained transformer knowledge.
Hands-on Exercises (Optional): Fine-tuning a pre-trained transformer for a chosen NLP task relevant to your SCM domain (coding required).
Ethical Considerations of LLMs: Addressing bias, fairness, and potential misuse of transformers in SCM implementations.
Exploring the Future of Transformers: Applications beyond text analysis, exploring code generation for automating tasks in SCM (optional).
Prerequisites:
Strong understanding of linear algebra, calculus, and probability theory
Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)
Familiarity with basic NLP concepts and techniques (e.g., tokenization, word embeddings)
Knowledge of the original transformer architecture and its applications