Strategies for Practical Active Learning
Strategies for Practical Active Learning


In many real-world Machine Learning applications, you need to continually update your models with new training data to improve and maintain accuracy as your model is applied. However, it is often difficult to decide what new data needs to be labeled for training, and what is the best workflow and interfaces for labeling. This training will focus on how you can use Active Learning to improve your training data at scale with common Deep Learning frameworks. At the end of this session, you will understand several Active Learning strategies. We will use the example of applying Active Learning to the ImageNet data set using the TensorFlow Deep Learning framework.


Robert Munro is an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to Text, Speech, Image and Video Processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti and the Amazon, to London, Sydney and Silicon Valley, in organizations ranging from startups to the United Nations. He most recently ran Product for AWS's first Natural Language Processing services in the Deep Learning team at Amazon AI. He is currently the VP of Machine Learning at Crowdflower.

Robert has published more than 50 papers and is a regular speaker about technology in an increasingly connected world. He has a PhD from Stanford University.

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