Description
Build a Deep Learning ChatBot That Understands Context—Not Just Keywords
Chatbots are everywhere—but most are dumb rule-based systems that fail the moment a user says something unexpected. This course teaches you to build a deep learning chatbot using sequence-to-sequence (Seq2Seq) models, attention mechanisms, and real dialogue datasets—so your bot actually understands and responds like a human.
What You’ll Build
- A context-aware chatbot trained on movie dialogues (Cornell corpus)
- A Seq2Seq neural network with encoder-decoder architecture
- An attention mechanism that lets the bot focus on relevant parts of the input
- A TensorFlow pipeline for training and inference
Step-by-Step Journey
- Data preprocessing—cleaning, tokenizing, and vectorizing conversational text
- Building the encoder—a GRU/LSTM that compresses input into context vectors
- Building the decoder—a recurrent network that generates responses token by token
- Adding attention—so the decoder knows which words to focus on
- Training and tuning—handling teacher forcing, gradient clipping, and BLEU scoring
- Testing and improving—making your bot less repetitive and more contextually relevant
Why Deep Learning for Chatbots?
- Handles ambiguity—understands “Yeah, right” as sarcasm, not agreement
- Learns from data—no manual rule writing
- Scales with conversation depth—supports multi-turn dialogues
Who Is This For?
- Intermediate Python developers with basic deep learning knowledge
- NLP enthusiasts wanting to go beyond sentiment analysis
- Students building AI capstone projects
- Entrepreneurs automating customer support
Beyond the Hype—Real Skills for Real AI
This isn’t a “drag-and-drop bot” course. You’ll write the code, debug the gradients, and understand why your bot says what it says. That’s the difference between a demo and a deployable product.
Ready to build the next generation of conversational AI? Enroll now.
