Software engineer at TomTom by day, machine learning enthusiast at night. My leading technology is Java and Java-based frameworks. On a daily basis, I work on designing, implementing and deploying distributed systems that work in cloud environments, such as Microsoft Azure and AWS. I'm interested in classification problems and multi-agent systems. I love to learn, read books and play football – in no particular order.Back to speakers list
Machine Learning: The Bare Math Behind Libraries
Machine learning one of the most innovative fields in computer science – yet people use libraries as black boxes. We will start by defining what machine learning is and equip you with an intuition of how it works. Then we'll explain gradient descent algorithm using linear regression and project it to supervised neural networks training. Within unsupervised learning, you will become familiar with Hebb’s learning and learning with concurrency. Our aim is to show the mathematical basics of neural networks for those who want to start using machine learning in their day-to-day work.