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.
Wee Hyong Tok is a principal data science manager with the AI CTO Office at Microsoft, where he leads the engineering and data science team for the AI for Earth program. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Predictive Analytics Using Azure Machine Learning and Doing Data Science with SQL Server. Wee Hyong holds a PhD in computer science from the National University of Singapore.
Danielle Dean is the technical director of machine learning at iRobot. Previously, she was a principal data science lead at Microsoft. She holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill.
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