Machine Learning

1. ML Goes Fruitful Workshop
Sudhir Verma, Preeti Negi, Deepika Sharma, Esha Ahuja, Vikash Sharma
June 13, 2017

2. Agenda
• Introduction
Machine Learning
Image Classification using TensorFlow
• Problem Statement
Object Detection Case Study for fruit salad.
• Hands-On End to End Solution
Training and Testing the model.
Studying the analytics.
Optimizing the trained model.
• Conclusion
Summary
Experience (Challenges) sharing by speakers.
Fig: Manual to Automation using Machine Learning

3. Architecture

4. Key Learnings
• Learning of TensorFlow library, python, docker, machine learning
classification techniques (parameters for testing accuracy), Raspberry
Pi/mobile app.
• Implementation of business use case in food processing industry.
• Understanding the integration of varied technologies such as python,
machine learning, IoT, mobile etc.

5. Key Takeaways
• Gaining confidence in machine learning to identify more scenarios in
different industry domains.
• Feeling of achievement by creating end to end machine learning fruit
detection application.
• Gaining insights in the latest trends in technologies.

6. • Healthcare- identifying diseases like tumors, diabetes, cancer etc.
• Food Sector- provide useful information like nutritional value, quality etc.
of the fruits and vegetables.
• Agriculture Sector- identifying crop diseases or automation of sorting,
segregation and packaging.
• Retail- identifying the quality of the products and remov defective
products.
Vision and Business Use Cases Identified

7. Hands-On: Live Demo Mobile App
• Mango
• 0.9976
017
• Apple
• 0.9934278
7
Mango
0.9976017
Apple
0.9934278

8. Live Demo: Analyzing trained model using
TensorBoard

9. Live Demo: Analyzing TensorBoard Graphs

10. LiveDemo: Final Test Accuracy

11. Thank you.
Sudhir Verma, Preeti Negi, Deepika Sharma, Esha Ahuja, Vikash Sharma