Stanford CS224N Deep Learning NLP
Research and implementation of deep learning NLP models and transformers
Comprehensive research repository implementing core architectures from Stanford's CS224N: Natural Language Processing with Deep Learning. Implements Word2Vec skip-gram negative sampling, neural dependency parsers, Seq2Seq with attention, and Transformer attention heads.
The Challenge
Mastering modern state-of-the-art Natural Language Processing requires understanding math-first fundamentals—from loss gradients in Word2Vec and softmax temperature down to multi-head self-attention mechanisms and beam search decoding.
Architecture & Solution
Developed clean, documented PyTorch implementations of the complete Stanford CS224N curriculum, implementing foundational neural network models from mathematical formulas to validated code with benchmark evaluations.
Engineering Decisions
- From-Scratch Mathematical ImplementationsWrote gradient checks, word vector projections, and attention weighting matrices from scratch before utilizing high-level PyTorch modules.
- Modular Training LoopsBuilt standardized training pipelines with learning rate scheduling, gradient clipping, and perplexity tracking.
Outcomes
- 16 stars on GitHub as a reference study guide for university NLP researchers
- Directly informed the architectural principles applied in the LingualDub framework