Introduction to Transformer Models
Chapter 1: Sequence Modeling and Attention Fundamentals
Challenges in Sequence-to-Sequence Tasks
Recap: Recurrent Neural Networks (RNNs)
Limitations of Traditional RNN Approaches
Introducing the Attention Mechanism Concept
Attention Score Calculation: A High-Level View
Context Vectors from Attention Weights
Quiz for Chapter 1
Chapter 2: Self-Attention and Multi-Head Attention
The Idea Behind Self-Attention
Query, and Value Vectors in Self-Attention
Scaled Dot-Product Attention Mechanism
Visualizing Self-Attention Scores
Introduction to Multi-Head Attention
How Multi-Head Attention Works
Benefits of Multiple Attention Heads
Hands-on Practical: Implementing Scaled Dot-Product Attention
Quiz for Chapter 2
Chapter 3: The Transformer Encoder-Decoder Architecture
Overall Architecture Overview
Input Embedding Layer
The Need for Positional Information
Positional Encoding Explained
The Encoder Stack
Add & Norm Layers (Residual Connections)
Position-wise Feed-Forward Networks
The Decoder Stack
Masked Multi-Head Self-Attention
Encoder-Decoder Attention Mechanism
Final Linear Layer and Softmax
Hands-on Practical: Building an Encoder Layer
Quiz for Chapter 3
Chapter 4: Training and Implementing Transformers
Data Preparation: Tokenization
Creating Input Batches
Loss Functions for Sequence Tasks
Optimization Strategies
Regularization Techniques
Overview of a Basic Implementation
Using Pre-trained Model Libraries (Brief)
Practice: Assembling a Basic Transformer
Quiz for Chapter 4
Add & Norm Layers (Residual Connections)
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- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc.) DOI: 10.5555/3295222.3295349 - The foundational paper introducing the Transformer architecture, detailing the integration of residual connections and layer normalization within its encoder-decoder blocks.
- Deep Residual Learning for Image Recognition, Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR.2016.90 - Introduces residual networks (ResNet), which first proposed the concept of residual connections to enable the training of very deep neural networks by mitigating vanishing gradients.
- Layer Normalization, Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton, 2016 arXiv preprint arXiv:1607.06450 DOI: 10.48550/arXiv.1607.06450 - Presents Layer Normalization, a technique for normalizing activations within a layer across features, which is crucial for stabilizing training in recurrent and sequence models like the Transformer.