Scalable Machine Learning and Deep Learning with H2O

At this meetup we'll have a guest from London - Jo-Fai Chow. Jo-Fai is working for H20.ai, the world’s leading open source deep learning platform. H2O is used by over 80,000 data scientists and more than 9,000 organizations around the world.

Since we'll have two talks, there will be a pizza break (tnx H20!) :) As always, free drinks and coffee are provided by our hosts - Seven Bridges.

Introduction to Scalable Machine Learning with H2O

This talk will be an overview of H2O.ai, its open-source machine learning platform as well as its new projects - Deep Water and Steam. Jo-Fai will also explain how can you do scalable Machine Learning using H20. If you are not familiar with H2O, this presentation will give you enough information about it.

Deep Water - Making Deep Learning Accessible to Everyone

Deep Water is H2O's integration with multiple open source deep learning libraries such as TensorFlow, MXNet and Caffe. On top of the performance gains from GPU backends, Deep Water naturally inherits all H2O properties in scalability, ease of use and deployment. In this talk, Joe will go through the motivation and benefits of Deep Water. After that, he will demonstrate how to build and deploy deep learning models with or without programming experience using H2O's R/Python/Flow (Web) interfaces.

Lecturer: Jo-Fai Chow (https://www.linkedin.com/in/jofaichow/) (or simply Joe) is a Data Scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in the US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in the UK and abroad. He also holds an MSc in Environmental Management and a BEng in Civil Engineering.