Introduction to Transformer Models for NLP: Unit 3

Overview

This course covers transformer models and their applications in natural language processing and computer vision. Topics include the T5 model, fine-tuning for tasks such as abstractive summarization, and the Vision Transformer. Students will learn to build an image captioning system by combining vision and language models. The course also provides practical instruction on deploying models, including MLOps practices, sharing models on HuggingFace, and cloud deployment with FastAPI. By the end of the course, students will have the knowledge and skills to implement, fine-tune, and deploy transformer models for various real-world tasks.

Syllabus

Introduction to Transformer Models for NLP: Unit 3

This module explores advanced transformer models and their applications across natural language processing and computer vision. Learners will examine the T5 model’s end-to-end architecture and cross-attention mechanism, apply and fine-tune T5 for complex NLP tasks, and discover how vision transformers extend these techniques to image processing and image captioning. The module concludes with practical strategies for deploying and sharing transformer models using MLOps principles, HuggingFace, and FastAPI, equipping students with both theoretical understanding and hands-on skills for state-of-the-art model development and deployment.