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 the Healthcare & Life Sciences industries. 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 the healthcare and life sciences domains, such as biomedical text classification and sequence modeling.

• 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 Healthcare & Life Sciences tasks

• Evaluate and interpret the results of Transformer-based models in healthcare and life sciences contexts

1. Introduction to Transformer Models

• Limitations of traditional sequence-to-sequence models (RNNs, LSTMs)

• 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

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

• Fine-tuning strategies for downstream tasks

• Adapting Transformer models for Healthcare & Life Sciences tasks

• Hands-on exercises: Fine-tuning a pre-trained Transformer model for biomedical text classification

3. Transformer-based Sequence Modeling

• Transformer-based models for sequence modeling tasks

• Applying Transformers to biomedical sequence data (e.g., DNA, RNA, protein sequences)

• Attention visualization and interpretation techniques

• Hands-on exercises: Implementing a Transformer-based model for protein function prediction

4. Evaluation and Interpretation

• Evaluation metrics for Transformer-based models in Healthcare & Life Sciences

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

• Case studies of Transformer-based models in healthcare and life sciences applications

• Hands-on exercises: Evaluating and interpreting Transformer model results on biomedical 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