Demystifying transfer learning with Tensorflow
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In many organizations, a lot of data and work goes into training models in machine learning. The cost of the model training also becomes very high if the model becomes complex. Complex machine learning models can only be made with years of experience and it becomes difficult for Machine learning and AI engineers to make the models more efficiently and quickly. This is where transfer learning comes into the frame to help them. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is an opportunistic way of reducing machine learning model training to be a better steward of an organization's resources. In this webinar slides, you will discover how you can use transfer learning to speed up training and improve the performance of your deep learning model.
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Demystifying transfer learning with Tensorflow
- 1. Demystifying transfer learning with TensorFlow Presented By: Shubham Goyal Data Scientist Knoldus Inc. & MachineX Intelligence
- 2. 2 Our Agenda 01 Introduction to MachineX 02 Introduction to Transfer Learning 03 Implement transfer learning with tensorflow 04 Best practices to implement transfer learning
- 3. 3 About Knoldus MachineX MachineX is a group of data wizards. We are a team of Data Scientist and engineers with a product mindset who deliver competitive business advantage.
- 4. Our Global Presence 8+ Years Years of Profitable Growth 155+ People Largest Scala + Spark + Tensorflow + Pytorch Services Company 04 Offices Offices globally 17+ Customers Multi-year Global Customers
- 5. Our Partners Through our strategic partnerships, we have an unwavering commitment to equip your organization with the knowledge, skills, expertise, resources and tools to succeed.
- 6. Knoldus MachineX Offerings Natural Language Processing Computer Vision Solutions Data mining Chatbot Development Artificial intelligence research and solutions
- 7. 7 An Intelligent Meeting Assistant Application Record Videos View DashBoardAn Intelligent Meeting Assistant Application
- 8. 8
- 9. 9 Machine learning library in scala KSAI FishEye
- 10. 10 Enable organizations to capture new value and business capabilities Innovation Labs Consistently blogging, to share our knowledge, research Blogs Deeplearning, Coursera, Stanford certified professionals Certifications Insight & perspective to help you to make right business decisions TOK Sessions It’s great to contribute back to the community. We continuously advance open source technologies to meet demanding business requirements. Open Source Contribution
- 11. Image processing
- 13. Traditional Way Traditional pipeline for image classification involves two modules ● Feature extraction ● Classification
- 14. Problems Problems with this pipeline ● Feature extraction cannot be tweaked according to the classes and images ● Completely different from how we humans learn to recognize things.
- 15. Solution?????
- 16. Convolutional Neural Network (CNN, or ConvNet)
- 17. ● Convolutional base ● Classifier
- 20. Healthcare
- 21. Manufacturing
- 23. 23
- 24. 24
- 25. 25 The Application of skills, knowledge, and/or attitudes that were learned in one situation to another learning situation transfer learning is usually expressed through the use of pre-trained models
- 26. 26 Transfer of learning (from teacher to student)
- 28. Problems The problem was ● less learned rate in each generation ● Number of knowledge amount passed down was less
- 30. Difference
- 31. Transfer Learning: Making Less Data Cool Again Less data !!!!!!!
- 33. 33 edges, shapes, and different intensities of light and dark pixels
- 34. 34
- 35. Understanding various architectures of Convolutional Networks ResNet, AlexNet, VGGNet, Inception
- 36. 36 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) CNN architectures of ILSVRC top competitors
- 37. © 2018 Minimal – Presentation template 37 There are various architectures of CNNs available LeNet AlexNet VGGNet GoogLeNet ResNet
- 38. Implement transfer learning with TensorFlow