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
Limitations of Traditional RNN Approaches
Was this section helpful?
- On the difficulty of training recurrent neural networks, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio, 2013 Proceedings of the 30th International Conference on Machine Learning (ICML) DOI: 10.1137/1.9781611973001.27 - Discusses challenges in training recurrent neural networks, particularly vanishing and exploding gradients.
- Long Short-Term Memory, Sepp Hochreiter, Jürgen Schmidhuber, 1997 Neural Computation, Vol. 9 DOI: 10.1162/neco.1997.9.8.1735 - Introduces the Long Short-Term Memory (LSTM) architecture to address long-term dependencies in RNNs.
- Sequence to Sequence Learning with Neural Networks, Ilya Sutskever, Oriol Vinyals, Quoc V. Le, 2014 Advances in Neural Information Processing Systems (NIPS) 27 - Presents a general end-to-end approach for sequence learning, illustrating the encoder-decoder structure with a fixed-size context.
- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems (NIPS) 30, Vol. 30 (Curran Associates, Inc.) DOI: 10.48550/arXiv.1706.03762 - Introduces the Transformer model, designed to overcome limitations of recurrent models like sequential computation and long-range dependency capture.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - Provides a foundation on recurrent neural networks, backpropagation through time, and associated training difficulties.