Description
NLP with Deep Learning – Winter 2019 (Stanford CS224N Style)
This course preserves the **legendary Stanford CS224N curriculum from Winter 2019**—the exact moment NLP shifted from RNNs to transformers. You’ll implement word2vec, seq2seq, attention, and early BERT variants **from scratch in PyTorch**, with the same rigor used to train AI leaders at Google and Meta.
What You’ll Build
- A word2vec model trained on Wikipedia
- A machine translation system using seq2seq + attention
- A question-answering model inspired by early BERT
- A sentiment analyzer with contextual embeddings
Key Modules
- Word embeddings—word2vec, GloVe, fastText
- RNNs & LSTMs—for sequence modeling and language modeling
- Neural machine translation—encoder-decoder, beam search
- Attention mechanisms—Bahdanau, Luong, self-attention
- Contextual embeddings—ELMo, early BERT concepts
Why Winter 2019?
- Historical turning point—the last NLP course before BERT dominated everything
- Deep understanding—you’ll learn why transformers work by first mastering what they replaced
- Interview gold—FAANG still tests RNNs and attention in depth
Who Should Enroll?
- NLP engineers needing foundational knowledge
- Graduate students in computational linguistics
- Researchers replicating pre-LLM baselines
- Competitive programmers tackling NLP challenges
Understand NLP’s Evolution—So You Can Shape Its Future
This course gives you the **context and code** to truly understand modern NLP—not just use it.
Ready to learn NLP the Stanford way? Enroll now.
