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 Finance & Insurance 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 finance and insurance, such as time series forecasting, risk assessment, and natural language processing.

• 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 Finance & Insurance tasks

• Apply Transformer-based models to time series forecasting, risk assessment, and natural language processing tasks

• Evaluate and interpret the results of Transformer-based models in finance and insurance 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 Finance & Insurance tasks

• Hands-on exercises: Fine-tuning a pre-trained Transformer model for sentiment analysis of financial news

3. Transformer-based Time Series Modeling

• Transformer-based models for time series forecasting

• Applying Transformers to stock price prediction and volatility forecasting

• Attention visualization and interpretation techniques

• Hands-on exercises: Implementing a Transformer-based model for stock price prediction

4. Natural Language Processing with Transformers

• Transformer-based models for natural language processing tasks

• Applying Transformers to text classification, named entity recognition, and question answering in finance and insurance contexts

• Case studies of Transformer-based models in finance and insurance applications

• Hands-on exercises: Fine-tuning a Transformer model for classification of insurance claims

• 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