Week 5: Transformers

Week 5: Transformers#

Introduction#

Welcome to Week 5 of the course! This week, we will explore the Transformer architecture, a revolutionary model that has significantly advanced the field of Natural Language Processing. The Transformer introduces the concept of attention mechanisms, allowing models to capture relationships in data more effectively than traditional recurrent neural networks.

Learning Objectives#

By the end of this week, you should be able to:

  • Understand the fundamental concepts of the Transformer architecture.

  • Explain how the attention mechanism works within Transformers.

  • Analyze the structure and components of Transformer models.

  • Appreciate the impact of Transformers on modern NLP applications.

Key Learning Content#

Attention Mechanism#

  • Self-Attention: Learn how models focus on different parts of the input sequence to generate a representation.

  • Multi-Head Attention: Understand how multiple attention mechanisms operate in parallel to capture diverse relationships.

Transformer Structure#

  • Encoder and Decoder Modules: Explore the roles of the encoder and decoder in processing input and generating output.

  • Positional Encoding: Learn how Transformers handle sequential data without recurrent layers.

  • Feed-Forward Networks: Study the fully connected layers that process the attention outputs.

Lecture Details#

  • Format: Lecture and in-depth analysis of the Transformer model structure.

  • Topics Covered:

    • Limitations of traditional RNNs and the need for attention mechanisms.

    • Detailed walkthrough of the “Attention is All You Need” paper.

    • Visualization and dissection of the Transformer components.

    • Discussion on the advantages of Transformers over previous architectures.

Practice Activities#

  • Model Analysis: Break down the Transformer architecture into its constituent parts and understand their functions.

  • Visualization Exercises: Use tools to visualize attention scores and see how the model focuses on different input elements.

  • Optional Coding Task: Implement a simple attention mechanism or a miniature Transformer model using Python libraries like PyTorch or TensorFlow.

Resources#

Assignment#

  • Weekly Practical Assignment (Due Week 6):

    • Task: Analyze a Transformer model by explaining each of its components and how they contribute to the model’s ability to process language.

    • Submission: A written report (2-3 pages) including diagrams and explanations.

    • Evaluation Criteria:

      • Clarity of explanations

      • Depth of analysis

      • Use of supporting visuals

Notes#

  • Ensure you have completed the required readings before the lecture to maximize understanding.

  • Bring questions to the lecture for an interactive discussion session.

Looking Ahead#

Next week, we will begin working with LLM APIs, putting into practice the concepts learned about Transformers and attention mechanisms. Make sure you are comfortable with this week’s material, as it forms the foundation for the upcoming topics.