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Machine Learning Applied - Contextual Chatbots Coding, Oracle JET and Tensorflow

Learn how to build client side chatbot with Oracle JET, on top of contextual chatbot model, which is implemented with TensorFlow Learn library using deep neural network model. Context is king, while talking about intelligent chatbots. It is very important to keep context, otherwise conversation becomes useless. This session will focus on three areas - user intent processing with TensorFlow machine learning, conversation context tracking and chatbot UI implementation with Oracle JET. Communication between JET client side and TensorFlow is implemented through REST with Flask microframework for Python.

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Machine Learning Applied - Contextual Chatbots Coding, Oracle JET and Tensorflow

  1. 1. Machine Learning Applied - Contextual Chatbots Coding, Oracle JET andTensorFlow Andrejus Baranovskis, CEO andTechnical Expert, Red Samurai Consulting Oracle ACE Director and Oracle Developer Champion
  2. 2. Oracle ExpertsTeam ADF, JET, ORACLE FUSION, ORACLE CLOUD, MACHINE LEARNING Oracle PaaS Partner Community Award for Outstanding Java Cloud Service Contribution 2017
  3. 3. Session Goal HowTo BuildYour Own Machine Learning Chatbot
  4. 4. AGENDA • Technical Architecture • Solution WalkThrough • Machine Learning Introduction • Implementation Points
  6. 6. Machine Learning Chatbot Context Communication Chatbot UI Classification Chatbot messaging
  7. 7. Chatbot Custom application logic Generic listener
  8. 8. CHATBOT CONTEXT • Chatbot framework needs a structure in which conversational intents are defined (this can JSON file) • Conversational intent contains: • tag (unique name) • patterns (sentence patterns for neural network text classifier) • responses (one will be used as a response)
  11. 11. LEARNING AND INFERENCE Training data Feature vector Learning algorithm Model Test data Feature vector Model Prediction
  12. 12. REGRESSION Regression algorithm Input Output Continuous Continuous Discrete
  13. 13. REGRESSION w - parameter to be found usingTensorFlow
  14. 14. KEY PARAMETERS • Cost Function - score for each candidate parameter, shows sum of errors in predicting.The higher the cost, the worse the model parameters will be • Epoch - each step of looping through all data to update the model parameters • Learning rate - the size of the learning step
  15. 15. REGRESSION EXAMPLE w - parameter to be found usingTensorFlow
  16. 16. CLASSIFICATION f{x} Input Output DiscreteContinuous Discrete Classifier
  17. 17. CLASSIFICATION EXAMPLE Linear boundary line learned from the training data - equal probability for both groups
  18. 18. WHYTENSORFLOW? • TensorFlow has become the tool of choice to implement machine learning solutions • Developed by Google and supported by its flourishing community • Gives a way to easily implement industry-standard code
  20. 20. QUESTIONS
  21. 21. CONTACTS • Andrejus Baranovskis • Email: abaranovskis@redsamuraiconsulting.com • Twitter: @andrejusb • LinkedIn: https://www.linkedin.com/in/andrejus-baranovskis-251b392 • Web: http://redsamuraiconsulting.com
  22. 22. REFERENCES • Source Code - https://github.com/abaranovskis-redsamurai/shenzhen • Contextual Chatbot inTensorFlow - https://bit.ly/2pFbTw4 • TensorFlow Book - http://tensorflowbook.com/