Neural Networks and NLP
ECS7001P · Queen Mary University of London
Deep learning for NLP: neural network architectures, RNNs, LSTMs, attention mechanisms, transformers, and sequence-to-sequence models.
RNNLSTMAttentionTransformersBERTNMTNLG
Overview
This QMUL module covers the intersection of deep learning and natural language processing. From basic neural networks to attention mechanisms and transformers, with hands-on labs implementing key architectures.
Content & Resources
LAB
Lab 1: Neural Network Basics
Feed-forward networks, backpropagation
LAB
Lab 2: Word Embeddings
Word2Vec, GloVe implementations
LAB
Lab 3: RNNs and LSTMs
Sequence modeling for text
LAB
Lab 4: Attention & Transformers
Self-attention, multi-head attention