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

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This document discusses building a machine learning chatbot using Oracle JET and TensorFlow. It outlines the technical architecture including a chatbot framework to define conversational intents in JSON, and using Oracle JET for the UI and custom application logic. It then provides a gentle introduction to machine learning concepts like regression, classification, cost functions, and epochs. Finally, it discusses implementation points and provides contact details for the presenter.

Software

Machine Learning Applied - Contextual Chatbots, Oracle JET and TensorFlow
Andrejus Baranovskis
Founder at Katana ML/Oracle Expert at Red Samurai Consulting
@andrejusb

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

Session Goal
HowTo BuildYour Own Machine Learning Chatbot

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

Machine Learning Chatbot Context Communication Chatbot UI
Classiﬁcation Chatbot messaging
Oracle JET

Chatbot Custom application logic
Generic listener
Oracle JET

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)

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

REGRESSION
Regression
algorithm
Input Output
Continuous
Continuous
Discrete

REGRESSION
w - parameter to be found usingTensorFlow

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

REGRESSION EXAMPLE
w - parameter to be found usingTensorFlow

CLASSIFICATION
f{x}
Input Output
DiscreteContinuous
Discrete
Classiﬁer

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

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

CONTACTS
• Andrejus Baranovskis
• Email: abaranovskis@redsamuraiconsulting.com
• Twitter: @andrejusb
• Web: https://www.katanaml.io/

REFERENCES
• Source Code - https://github.com/katanaml/katana-assistant
• Blog post - http://andrejusb.blogspot.com/2018/07/contextual-
chatbot-with-tensorﬂow.html

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1. Machine Learning Applied - Contextual Chatbots, Oracle JET and TensorFlow Andrejus Baranovskis Founder at Katana ML/Oracle Expert at Red Samurai Consulting @andrejusb

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 Oracle JET

7. Chatbot Custom application logic Generic listener Oracle JET

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

10.

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

21. CONTACTS • Andrejus Baranovskis • Email: abaranovskis@redsamuraiconsulting.com • Twitter: @andrejusb • Web: https://www.katanaml.io/

22. REFERENCES • Source Code - https://github.com/katanaml/katana-assistant • Blog post - http://andrejusb.blogspot.com/2018/07/contextual- chatbot-with-tensorﬂow.html

23. 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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