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

Oracle Code 2019 Online session

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

  1. 1. Machine Learning Applied - Contextual Chatbots, Oracle JET and TensorFlow Andrejus Baranovskis Founder at Katana ML/Oracle Expert at Red Samurai Consulting @andrejusb
  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
  5. 5. TECHNICAL ARCHITECTURE
  6. 6. Machine Learning Chatbot Context Communication Chatbot UI Classification Chatbot messaging Oracle JET
  7. 7. Chatbot Custom application logic Generic listener Oracle JET
  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)
  9. 9. SOLUTION WALKTHROUGH
  10. 10. GENTLE INTRODUCTIONTO MACHINE LEARNING
  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
  19. 19. IMPLEMENTATION POINTS
  20. 20. CONTACTS • Andrejus Baranovskis • Email: abaranovskis@redsamuraiconsulting.com • Twitter: @andrejusb • Web: https://www.katanaml.io/
  21. 21. REFERENCES • Source Code - https://github.com/katanaml/katana-assistant • Blog post - http://andrejusb.blogspot.com/2018/07/contextual- chatbot-with-tensorflow.html
  22. 22. Machine Learning Applied - Contextual Chatbots, Oracle JET and TensorFlow Andrejus Baranovskis Founder at Katana ML/Oracle Expert at Red Samurai Consulting @andrejusb

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