Advanced Transformers

This course is designed to provide a comprehensive understanding of advanced transformer architectures and their applications in the Healthcare & Life Sciences industries. Participants will learn about the latest developments in transformer models, such as attention mechanisms, pre-training techniques, and domain-specific adaptations, enabling them to develop and deploy state-of-the-art natural language processing (NLP) and sequence modeling solutions for various tasks relevant to the healthcare and life sciences domains, such as biomedical text mining, clinical decision support, and drug discovery.

• Understand the principles and techniques behind advanced transformer architectures

• Implement and fine-tune pre-trained transformer models, such as BERT, GPT, and T5, for healthcare and life sciences tasks

• Apply domain-specific adaptations to transformer models, such as incorporating biomedical knowledge and multi-modal data

• Develop transformer-based solutions for biomedical text mining, clinical decision support, and drug discovery

• Deploy transformer models for real-world applications in the Healthcare & Life Sciences industries

1. Attention Mechanisms and Pre-training

• Overview of attention mechanisms and their role in transformers

• Self-attention, multi-head attention, and cross-attention

• Pre-training objectives: masked language modeling, next sentence prediction, and contrastive learning

• Hands-on exercises: Implementing attention mechanisms and pre-training techniques from scratch

2. Advanced Transformer Architectures

• BERT (Bidirectional Encoder Representations from Transformers) and its variants for biomedical NLP

• GPT (Generative Pre-trained Transformer) and its applications in clinical text generation

• T5 (Text-to-Text Transfer Transformer) and its unified framework for healthcare NLP tasks

• Hands-on exercises: Fine-tuning pre-trained transformer models for biomedical text classification and named entity recognition

3. Domain-Specific Adaptations

• Incorporating biomedical knowledge (e.g., ontologies, knowledge graphs) into transformer models

• Multi-modal transformers for integrating clinical text, images, and structured data

• Domain-adaptive pre-training and transfer learning for healthcare-specific language models

• Hands-on exercises: Developing domain-specific transformer models for clinical decision support and patient outcome prediction

4. Applications in Drug Discovery and Precision Medicine

• Transformer-based models for drug-target interaction prediction and virtual screening

• Molecular property prediction and de novo drug design using transformers

• Personalized medicine and patient stratification with transformer models

• Hands-on exercises: Building a transformer-based solution for drug discovery or precision medicine

5. Deployment and Practical Considerations

• Deploying transformer models in production environments for real-time inference

• Optimizing transformer models for resource-constrained settings (e.g., edge devices, mobile platforms)

• Monitoring and updating deployed models for continuous improvement

• Ethical considerations and interpretability techniques for transformer models in healthcare

• Hands-on exercises: Deploying a transformer model using a cloud platform (e.g., AWS, GCP) and integrating it with a clinical workflow

• 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