Natural Language Processing with Attention Models Course Syllabus

A comprehensive course that empowers learners to master attention mechanisms and Transformer models in NLP, blending theory with practical application. This course is structured into three core modules followed by a hands-on final project, totaling approximately 26 hours of learning. Each module builds on foundational knowledge to progressively develop advanced NLP systems using attention-based architectures, with a strong emphasis on real-world implementation and coding exercises.

Module 1: Neural Machine Translation with Attention

Estimated time: 7 hours

• Limitations of traditional sequence-to-sequence models
• Introduction to attention mechanisms in NLP
• Building an attention-based neural machine translation system
• Translating English to German with attention-enhanced models

Module 2: Text Summarization with Transformers

Estimated time: 8 hours

• Comparing RNNs and Transformer architectures
• Implementing self-attention and multi-head attention
• Understanding positional encoding in Transformers
• Building a Transformer model for text summarization

Module 3: Question Answering with Pre-trained Models

Estimated time: 11 hours

• Introduction to transfer learning in NLP
• Using BERT for context-based question answering
• Leveraging T5 for generative question answering
• Evaluating model performance on QA tasks

Module 4: Understanding Attention Mechanisms

Estimated time: 5 hours

• Deep dive into self-attention and causal attention
• Multi-head attention and its role in model expressiveness
• Attention weights visualization and interpretation

Module 5: Transformer Architecture Internals

Estimated time: 6 hours

• Encoder-decoder framework in Transformers
• Position-wise feed-forward networks
• Layer normalization and residual connections

Module 6: Final Project

Estimated time: 10 hours

• Build an end-to-end NLP application using attention models
• Implement a system for translation, summarization, or question answering
• Submit code and a short report demonstrating model performance

Prerequisites

• Proficiency in Python programming
• Familiarity with foundational machine learning concepts
• Basic understanding of neural networks and deep learning

What You'll Be Able to Do After

• Implement encoder-decoder architectures with attention for machine translation
• Build Transformer models for text summarization tasks
• Utilize pre-trained models like BERT and T5 for question-answering systems
• Understand and apply self-attention, causal attention, and multi-head attention
• Develop and deploy advanced NLP systems using state-of-the-art techniques