Programmer, retired mage, bookworm, storyteller and liberal arts devotee. I'm into *language semantics*, its understanding and impact on the way people think. I love both natural and programming languages - professionally my heart belongs to *Java*, but I cheat on her with *Python*, *Scala* and, occasionally, other beautiful languages. In addition to my work at _TomTom_ as a software engineer a I'm keen on artificial intelligence, mainly for natural language understanding. If we are to reach technological singularity, we better get on it!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.