Neural Networks and NLP
Deep learning for NLP: neural network architectures, RNNs, LSTMs, attention mechanisms, transformers, and sequence-to-sequence models.
RNNLSTMAttentionTransformersBERTNMTNLG
Overview
The intersection of deep learning and natural language processing. From basic neural networks to attention mechanisms and transformers, with hands-on implementations of key architectures.
Content & Resources
PRACTICE
Neural Network Basics
Feed-forward networks, backpropagation
PRACTICE
Word Embeddings
Word2Vec, GloVe implementations
PRACTICE
RNNs and LSTMs
Sequence modeling for text
PRACTICE
Attention & Transformers
Self-attention, multi-head attention