Machine Learning: The Bare Math Behind Libraries - Unsupervised Learning (part 2)

Algorithms • May 2020

About

Machine Learning: The Bare Math Behind Libraries - Unsupervised Learning (part 2)

About

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 (part 2), 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.

Language

English

Level

Intermediate

Length

35 minutes

Type

webinar

About the speakers

Łukasz Gebel

Senior Software Engineer • TomTom

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.

Piotr Czajka

Expert Software Engineer • TomTom

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!

Details

Language

English

Level

Intermediate

Length

35 minutes

Type

webinar