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1
Intro
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About me
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FAQ Assistance
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Personalized Assistance
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Use Cases
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Personal Experience
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Banking bots
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Machine learning
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What is intended recognition
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Bot definition
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Intent recognition
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Named entities
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Dialogue management
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Conversational design
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Define your problem
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Data driven dialog systems
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Data augmentation
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TFIDF
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Embeddings
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Handling fallback
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Handling misclassified intent
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Using generative models
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Combining generative models with intent classification
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Is generative models good
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Feedback mechanism
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Examples
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trickiest part of working with chatbots
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stop words
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negations
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multiple intents
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presentation slides
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RAZ vs Dialogflow
Description:
Explore the world of conversational agents in this comprehensive workshop led by Merve Noyan, a Machine Learning Engineer and Google Developer Expert. Gain insights into building your own bot tailored to your specific use case. Delve into frameworks, state-of-the-art natural language understanding, dialogue management, and generative models. Learn about common pitfalls in chatbot development, from training data to production monitoring. Discover the intricacies of intent recognition, named entities, and conversational design. Examine data-driven dialog systems, data augmentation techniques, and methods for handling fallbacks and misclassified intents. Evaluate the pros and cons of generative models and their integration with intent classification. Tackle challenging aspects of chatbot development, including stop words, negations, and multiple intents. Gain valuable insights from real-world examples and compare popular frameworks like RASA and Dialogflow.

BYOB - Build Your Own Bot

Abhishek Thakur
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