This section is under construction. Content is being migrated from source materials.
Topics
RNN Fundamentals
Recurrent neural networks for sequence modeling, including simple RNNs and the challenges of learning long-term dependencies.
LSTM Architecture
Long Short-Term Memory networks with gates for controlling information flow and solving the vanishing gradient problem.
Transformers
Self-attention mechanisms that enable parallel processing and capture long-range dependencies in sequences.
Attention Mechanisms
Single-head and multi-head attention for learning contextual representations of tokens.
Key Concepts
- Sequence Modeling: Processing variable-length input sequences
- Word Embeddings: Dense vector representations of tokens (word2vec, GloVe)
- Recurrence: Hidden state evolution for capturing temporal dependencies
- Self-Attention: Mechanism for relating different positions in a sequence
- Positional Encoding: Adding sequence order information to embeddings
- Transformer Blocks: Stacking attention and feed-forward layers
Learning Outcomes
After completing this section, you will be able to:- Understand the architecture and training of recurrent neural networks
- Explain how LSTM gates address the vanishing gradient problem
- Implement self-attention mechanisms from scratch
- Describe the transformer architecture and its advantages over RNNs
- Apply pre-trained language models to downstream NLP tasks