Victor Hernandez is a data science and machine learning consultant at B3 Commit since 2019. Before joining B3, he was a postdoctoral researcher in Örebro University where he worked with machine learning applied to mobile robotics and environmental monitoring. Victor has published several scientific articles in peer review journals and conferences and his work has been recognized with different international awards. In 2013, he received the Best Service Robot Paper Award at ICRA, which is arguably the largest robotics conference worldwide and in 2012, he received the award of distinction for environmental contributions (from Clearpath robotics) for his research on green robotics. Now as a B3 consultant, Victor has worked in the development of project proposals and proof-of-concepts that involve the use of machine learning/A.I. and he is always eager to collaborate and exchange ideas with his peers.
The do’s and don’ts of practical machine learning development
Nowadays Machine Learning (ML) is one of the hottest topics in the industry. Exciting projects are showcased in the media almost every day, ranging from face/object recognition to medical applications and autonomous cars. While ML systems are not black boxes that seamlessly produce cool results, you don't need a Ph.D. in mathematics to start developing your very first project! The goal of this talk is to provide you with a basic tool set that will allow you to bring ML into practice. By using these tools, you can avoid pitfalls that are often encountered by ML practitioners and that can derail your ML developer's journey. With open-source in mind, this talk will also give you practical suggestions on how to find libraries, datasets and related articles that can boost your ML game.