How to apply your idea from self-supervised learning to a particular problem?
Join us for our February event for data practitioners where Daniel Mika, an entrepreneur and machine learning applied scientist, will share his approaches and experience on self-supervised learning in the context of computer vision.
Self-supervised learning often enables engineers and applied scientists to deliver better results with small amounts of labeled data, which makes these methods very relevant in business, especially in startups or early products settings.
Among others, Daniel will share:
- The basic intuition and ideas behind self-supervised learning.
- Particular techniques that you can use to boost your model quality with use of self-supervised learning.
- How to apply self-supervised learning in an industry setting.
Daniel will conclude the talk with a Q&A session, in which he will also help you to brainstorm experiment ideas.
The event will take place on February 22, at 7 pm in the dyvenia offices (Equal Business Park - Regus, Wielicka 28B, Krakow).
The event is free of charge, but we kindly ask you to confirm your attendance.
About Daniel:
Daniel Mika is an entrepreneur and machine learning applied scientist. In 2018, he co-founded getdressed startup -- a fashion-tech company which uses AI to match clothes into fashion outfits, power fashion personalization and exploration, and empower users to become fashion content creators. Daniel is the company’s CTO, leading engineering, research, data, product, and design functions at the company. He is studying business and engineering at the University of Pennsylvania (USA) in M&T dual degree program. He is a business generalist with deep specialization in machine learning, especially in computer vision, recommendation systems, and graph neural networks.
