Beyond Human Labeling and Supervised Learning w/ Google

Logo
Presented by

Karthik Ramasamy (Google) and Ned Martorell (Dataiku)

About this talk

[VIRTUAL REPLAY] Bringing data enthusiasts together to foster the exchange of ideas and the intellectual growth of the data community: Dataiku series of meetups showcase the work of talented data professionals across industries, for you to get insider tips and tricks to turn data into actionable insights (read more here (https://blog.dataiku.com/announcing-dataconnect-meetups-nyc). Typically most companies have been using supervised learning with human labeled dataset. However, thirst for more labels is never satisfied, especially for deep learning models. In this talk, Karthik will talk about technologies to overcome lack of abundance of human labeled dataset with specific examples in computer vision and NLP domains. The talk will explain how synthetic dataset can be used to train a model that can be generalized to operate on real world data. Then, Karthik will cover self-supervised learning that is becoming an emerging trend in large scale deep learning tasks. The talk will focus on a couple of use cases of self-supervised learning techniques that are general enough to be applicable in the majority of computer vision tasks.
Related topics:

More from this channel

Upcoming talks (1)
On-demand talks (265)
Subscribers (55913)
Dataiku is the platform for Everyday AI, enabling data experts and domain experts to work together to build data into their daily operations, from advanced analytics to Generative AI. Together, they design, develop and deploy new AI capabilities, at all scales and in all industries. Organizations that use Dataiku enable their people to be extraordinary, creating the AI that will power their company into the future. More than 600 companies worldwide use Dataiku, driving diverse use cases from predictive maintenance and supply chain optimization, to quality control in precision engineering, to marketing optimization, Generative AI use cases, and everything in between.