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

Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
11:05am-11:45am Thursday, September 6, 2018
Models and Methods
Location: Yosemite BC Level: Beginner
Secondary topics:  Computer Vision, Deep Learning models
Average rating: ***..
(3.33, 3 ratings)

Who is this presentation for?

  • Data scientists and developers

Prerequisite knowledge

  • A basic understanding of convolution neural networks (CNNs)

What you'll learn

  • Explore convolution neural networks (CNN) concepts
  • Learn how to visualize each layer of the network and use transfer learning for custom image classification and question-answering (QA) tasks

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
Photo of Danielle Dean

Danielle Dean

Microsoft

Danielle Dean is a principal data scientist lead at Microsoft in the Algorithms and Data Science Group within the Artificial Intelligence and Research Division, where she leads a team of data scientists and engineers building predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI Platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.

Photo of Wee Hyong Tok

Wee Hyong Tok

Microsoft

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.