ML Conference Munich super early bird offer ends March 5
ML Conference gives you the opportunity to learn about the latest machine learning tools and technologies, including TensorFlow 2.0, machine learning in the browser, deep learning and NLP. Seasoned ML experts will share insights on how to understand your data, optimize your models and more. The very early bird discount for ML Conference Munich ends on March 5, so why not take a look?
ML Conference Munich will take place from June 17-19, 2020. While the very early bird offer still stands, you can save more than €500 and get ML Intro Day for free with a 2-day pass as well as a 10% team discount for registering 3+ colleagues.
This year, ML Conference will take place three times in different locations across the world – Munich, Berlin and Singapore. Munich makes the start, with the conference taking place in June.
Check out the conference page for more information.
MLCon very early bird discount offers savings of more than €500. Very early bird registration ends on March 5, 2020. Plus, there’s also a few great extras included for those who strike while the iron is hot!
- ML Intro Day for free: Buy a 2-Day Pass and receive the ML Intro Day for free.
- Team Discount: Register 3+ colleagues and get 10 % off!
- Extra Special: Freelancers and employees of scientific institutions receive individual special conditions – please send us an email to [email protected].
ML Conference Munich tracks
The tracks are:
- Advanced ML Development
- Machine Learning Business & Strategy
- Machine Learning Principles
- Tools, APIs & Frameworks
ML Conference Munich program
The program is now online, and here are some highlights you can choose to attend:
AI with a devops mindset – experimentation, sharing and easier ML deployment – Thiago de Faria
An Introduction to Natural Language Generation – Christoph Henkelmann
Protecting AI Solutions From Attacks – Alexander Polyakov
Predicting Hotel Cancellations With Machine Learning: Classification and Time Series Analysis – Michael Grogan
Brains vs. Brawn – Is endless scaling really the way forward?
Christoph Henkelmann
Machine Learning 101++ Using Python
Dr. Pieter Buteneers
