It has been a while since our last meetup. Let's do one before the summer break. Thanks to our friends at Microsoft Reactor (again), we have a fantastic meetup venue in Central London.
- 18:00 to 18:30 Pizza time + networking
- Welcoming remarks by Joe Chow (H2O.ai)
- Talk 1: Time Series in Driverless AI by Marios Michailidis (H2O.ai)
- Talk 2: No more grid search! How to build models effectively by Thomas Huijskens (QuantumBlack)
- Talk 3: Self-adaptive tuning of the NN learning rate by James Wang
Time Series in Driverless AI by Marios Michailidis
Time-series forecasting is one of the most common and important tasks in business analytics. There are many real-world applications like sales, weather, stock market, energy demand, just to name a few. At H2O, we believe that automation can help our users deliver business value in a timely manner. Therefore, we combined advanced time series analysis and our Kaggle Grand Masters’ time-series recipes into Driverless AI.
In this talk, Marios will give an introduction to time series analysis in Driverless AI and then talk about all the latest development.
About H2O team in London:
- Marios Michailidis https://www.linkedin.com/in/mariosmichailidis/
- Jo-fai (Joe) Chow https://www.linkedin.com/in/jofaichow/
No more grid search! How to build models effectively by Thomas Huijskens
ll talk about recent advancements in model search strategies that are leveraged by many state-of-the-art AutoML systems.
Principal (Jr.) Data Scientist at QuantumBlack | McKinsey & Co
Self-adaptive tuning of the NN learning rate by James Wang
The learning rate is the most important hyper-parameter in neural networks. It involves many trials and errors to find the correct learning rate for your neural networks. The author had experience in adaptive control of industrial robotic manipulators. The purpose of this talk shows how to use control theory for the neural networks learning rate tuning.
Independent consultant in software development, databases et al. PhD in robotic control.