Transformers Basics

This course is designed to provide a comprehensive introduction to Transformer models, a groundbreaking architecture in deep learning, with a focus on their applications in Production Control and Operations (P&OC). Participants will learn the fundamental concepts behind Transformers, their advantages over traditional sequence-to-sequence models, and how to implement and fine-tune basic Transformer-based models for various tasks relevant to production scheduling, inventory management, and supply chain optimization.

• Understand the architecture and key components of Transformer models

• Implement and train basic Transformer models using deep learning frameworks

• Fine-tune pre-trained Transformer models for specific P&OC tasks

• Apply Transformer-based models to production scheduling, inventory management, and supply chain optimization tasks

• Evaluate and interpret the results of Transformer-based models in P&OC contexts

1. Introduction to Transformer Models in P&OC

• Limitations of traditional sequence-to-sequence models (RNNs, LSTMs) in P&OC

• Key components of Transformers: self-attention, multi-head attention, positional encoding

• Encoder-only architecture in Transformers

• Hands-on exercises: Implementing a basic Transformer encoder model

2. Fine-tuning Transformer Models for P&OC

• Pre-trained Transformer models (e.g., BERT, RoBERTa, DistilBERT)

• Fine-tuning strategies for downstream tasks in P&OC

• Adapting Transformer models for Production Control and Operations tasks

• Hands-on exercises: Fine-tuning a pre-trained Transformer model for production scheduling

3. Transformer-based Sequence Modeling in P&OC

• Transformer-based models for sequence modeling tasks in P&OC

• Applying Transformers to inventory management and supply chain optimization

• Attention visualization and interpretation techniques for P&OC tasks

• Hands-on exercises: Implementing a Transformer-based model for inventory management

4. Evaluation and Interpretation in P&OC

• Evaluation metrics for Transformer-based models in Production Control and Operations

• Techniques for interpreting Transformer model predictions (e.g., attention weights, saliency maps)

• Case studies of Transformer-based models in P&OC applications

• Hands-on exercises: Evaluating and interpreting Transformer model results on P&OC datasets

• 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

• Knowledge of production control and operations management principles