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.

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. Oracle ExpertsTeam
ADF, JET, ORACLE FUSION, ORACLE CLOUD, MACHINE LEARNING
Oracle PaaS Partner Community Award for Outstanding Java Cloud
Service Contribution 2017

3. Session Goal
HowTo BuildYour Own Machine Learning Chatbot

4. AGENDA
• Technical Architecture
• Solution WalkThrough
• Machine Learning Introduction
• Implementation Points

5. TECHNICAL ARCHITECTURE

6. Machine Learning Chatbot Context Communication Chatbot UI
Classification Chatbot messaging

7. Chatbot Custom application logic
Generic listener

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. SOLUTION WALKTHROUGH

11. GENTLE INTRODUCTIONTO MACHINE
LEARNING

12. LEARNING AND INFERENCE
Training data Feature vector Learning algorithm Model
Test data Feature vector Model Prediction

13. REGRESSION
Regression
algorithm
Input Output
Continuous
Continuous
Discrete

14. REGRESSION
w - parameter to be found usingTensorFlow

15. 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

16. REGRESSION EXAMPLE
w - parameter to be found usingTensorFlow

17. CLASSIFICATION
f{x}
Input Output
DiscreteContinuous
Discrete
Classifier

18. CLASSIFICATION EXAMPLE
Linear boundary line learned from the training data - equal probability for both groups

19. 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. IMPLEMENTATION POINTS

30. QUESTIONS

31. CONTACTS
• Andrejus Baranovskis
• Email: abaranovskis@redsamuraiconsulting.com
• Twitter: @andrejusb
• LinkedIn: https://www.linkedin.com/in/andrejus-baranovskis-251b392
• Web: http://redsamuraiconsulting.com

32. REFERENCES
• Source Code - https://github.com/abaranovskis-redsamurai/shenzhen
• Contextual Chatbot inTensorFlow - https://bit.ly/2pFbTw4
• TensorFlow Book - http://tensorflowbook.com/