Description
Deep Learning: Advanced NLP and RNNs
Move beyond sentiment analysis. This course dives into the **cutting-edge architectures** powering modern NLP: **sequence-to-sequence models**, **attention mechanisms**, **memory networks**, and the foundations of **transformers**. You’ll build systems that generate, translate, and reason about language—just like the models behind Google Translate and ChatGPT.
What You’ll Build
- A machine translation system (English to French) using seq2seq + attention
- A question-answering bot using memory networks
- A text summarizer that condenses long documents
- A neural chatbot with context-aware responses
Key Techniques Covered
- Bidirectional RNNs (BiRNNs)—capture context from past and future
- Sequence-to-sequence (Seq2Seq) models—encoder-decoder architecture
- Attention mechanisms—let the decoder focus on relevant input parts
- Memory Networks—store and retrieve facts for QA systems
- Beam search—generate higher-quality text outputs
Frameworks Used
- TensorFlow and PyTorch
- NumPy for from-scratch implementations
Why Learn This in 2025?
While transformers dominate headlines, **RNNs + attention** are still used in production for tasks with strict latency or memory constraints—and they’re essential for understanding how modern NLP evolved.
Who Is This For?
- Intermediate NLP practitioners
- Students in advanced ML courses
- Engineers prepping for NLP-focused roles
- Researchers exploring pre-transformer architectures
Your Path to Modern NLP Starts Here
This course bridges classical deep learning and the transformer era—giving you the full picture.
Ready to build intelligent language systems? Enroll now.
