Description
Build a TensorFlow-Powered Chatbot That Learns From Real Conversations
Move beyond template-based replies. This course teaches you to build a neural chatbot using TensorFlow and recurrent neural networks (RNNs) trained on real dialogue datasets—so your bot learns to respond naturally, not just match keywords.
What You’ll Build
- A neural conversational model inspired by Google’s 2015 research
- An RNN encoder-decoder that maps input sequences to output sequences
- A training pipeline using the Cornell Movie Dialogs Corpus
- A Flask web interface to test your bot in real time
Key Concepts Covered
- Sequence modeling—why RNNs are ideal for text
- Word embeddings—representing words as dense vectors
- Teacher forcing—a training trick to stabilize RNNs
- Beam search—generating higher-quality responses
- Evaluation—BLEU score vs. human judgment
Why This Approach?
While transformers power ChatGPT, RNN-based chatbots are still:
- Faster to train on small datasets
- Easier to debug for beginners
- Perfect for domain-specific bots (e.g., medical, legal, support)
Who Is This For?
- Intermediate Python developers with basic TensorFlow knowledge
- NLP students wanting hands-on sequence modeling experience
- AI hobbyists building personal assistants or game NPCs
From Research Paper to Working Prototype
You’ll implement the core ideas from “A Neural Conversational Model” (Vinyals & Le, 2015)—the paper that started the modern chatbot revolution.
Don’t just use chatbots—build them from the ground up. Enroll today.
