How to use transfer learning to bootstrap image classification and question answering (QA)
Transfer learning enables you to use pretrained deep neural networks and adapt them for various deep learning tasks (e.g., image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean to learn the secrets of transfer learning and discover how to customize these pretrained models for your own use cases.
| Talk Title | How to use transfer learning to bootstrap image classification and question answering (QA) |
| Speakers | Wee Hyong Tok (Microsoft), Danielle Dean (iRobot) |
| Conference | Artificial Intelligence Conference |
| Conf Tag | Put AI to Work |
| Location | San Francisco, California |
| Date | September 5-7, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Transfer learning enables you to use pretrained deep neural networks trained on various large datasets (ImageNet, CIFAR, WikiQA, SQUAD, and more) and adapt them for various deep learning tasks (e.g., image classification, question answering, and more). Wee Hyong Tok and Danielle Dean share the basics of transfer learning and demonstrate how to use the technique to bootstrap the building of custom image classifiers and custom question-answering (QA) models. You’ll learn how to use the pretrained CNNs available in various model libraries to custom build a convolution neural network for your use case. In addition, you’ll discover how to use transfer learning for question-answering tasks, with models trained on large QA datasets (WikiQA, SQUAD, and more), and adapt them for new question-answering tasks. Topics include:
