QCon San Francisco 2021 November 1-5, 2021 |

Abstract

Machine Learning applications need to continually update their models with new training data to improve and maintain accuracy. However, it is often difficult to decide what new data needs to be labeled for training, and what are the best workflow and interfaces for labeling. This talk 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 talk, you will understand several Active Learning strategies that you can apply for your business needs. We will use the example of applying Active Learning to the ImageNet data set using the TensorFlow Deep Learning framework.

Speaker: Robert Munro

VP of Machine Learning @CrowdFlower

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.