How to use transfer learning to bootstrap image classification and question answering (QA)

Description

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:

• An introduction to convolution neural networks and question-answering problems
• Using pretrained CNNs and the last fully connected layer as a featurizer (Once the features are extracted, any existing classifier can be used for image classification, using the extracted features as inputs.)
• Fine-tuning the pretrained models and adapting them for the new images
• Using pretrained QA models trained on large QA datasets (WikiQA, SQUAD) and applying transfer learning for QA tasks