Transformers Basics

This course introduces you to the revolutionary world of Transformer Models, a powerful architecture redefining Natural Language Processing (NLP) and impacting Supply Chain Management (SCM) through its ability to analyze vast amounts of textual data.

• Explain the core principles behind Transformer Models and their impact on NLP.

• Understand the key components of a Transformer architecture (encoder, decoder, attention mechanism).

• Explore how Transformers handle long-range dependencies in text data, crucial for SCM applications.

• Identify potential applications of Transformer Models in analyzing various forms of SCM data (e.g., customer reviews, demand forecasting, logistics documents).

• Gain insights into the pre-trained Transformer models readily available for use in SCM tasks.

1. Unveiling Transformer Models

• Introduction to Transformer Models: A Paradigm Shift in NLP.

• Demystifying the Transformer Architecture: Encoder, Decoder, and Attention Mechanism.

• Understanding how Transformers address long-range dependencies in text data.

• Interactive Exercises: Visualizing the attention mechanism and its role in analyzing text.

• Case Studies: Exploring early applications of Transformers in sentiment analysis of customer reviews for SCM.

2. Transformers and the Future of SCM

• Pre-trained Transformer Models: Leverage the power of pre-trained models like BERT for SCM tasks.

• Fine-tuning pre-trained models for specific SCM applications (e.g., demand forecasting from text data).

• Exploring the potential of Transformers in areas like intelligent document processing and chatbots for customer service.

• Discussion on the ethical considerations surrounding the use of NLP and Transformers in SCM.

• Strong understanding of machine learning concepts and algorithms

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with natural language processing and sequence modeling techniques