Topics
NLP Foundations
Text processing pipelines and word embeddings (word2vec).
RNN Fundamentals
Recurrent neural networks for sequence modeling and the challenges of learning long-term dependencies.
LSTM Architecture
Long Short-Term Memory networks with gates for controlling information flow.
Language Models
Statistical and neural language models for text generation.
Neural Machine Translation
Sequence-to-sequence models and encoder-decoder architectures.
Transformers
Self-attention mechanisms that enable parallel processing and capture long-range dependencies.
Key Concepts
- Word Embeddings: Dense vector representations of tokens (word2vec, GloVe)
- Sequence Modeling: Processing variable-length input sequences
- 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
- Multi-Head Attention: Using multiple attention heads to capture different aspects of input
- Transformer Blocks: Stacking attention and feed-forward layers
Learning Outcomes
After completing this chapter, you will be able to:- Build NLP pipelines and understand word embeddings
- Understand the architecture and training of recurrent neural networks
- Explain how LSTM gates address the vanishing gradient problem
- Implement sequence-to-sequence models for translation tasks
- Implement self-attention mechanisms from scratch
- Describe the transformer architecture and its advantages over RNNs