From Kaggle to H2O & Deep Learning for Time Series Prediction Meetup - By Jo-Fai Chow скачать в хорошем качестве

From Kaggle to H2O & Deep Learning for Time Series Prediction Meetup - By Jo-Fai Chow

In the first half of this video, Jo-Fai will share his joyful (yet sometimes very painful) Kaggle experience since joining the data mining competition platform. Coming from a rather traditional engineering background, data science was once like a complete myth to him. Joe will explain why participating in Kaggle is one of the most effective ways to kick-start a data science career. He will also explain how he used H2O for two Kaggle competitions: Rossmann Store Sales (2015) and Santander Product Recommendation (2016). View slides here: http://bit.ly/2lsrD3F Bio Jo-fai (or Joe) Chow 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 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 UK and abroad. He also holds a MSc in Environmental Management and a BEng in Civil Engineering. Abhishek's Talk In the second part of this talk, Abhishek will present his research in applying deep learning for time series prediction. He is focused on applying these new methods in the field of astronomy to light curves. View slides here: http://bit.ly/2mLX4qF Bio Abhishek Malali is a Master’s of Engineering student at Harvard University specializing in Computational Sciences. He focuses on applying machine learning research to time series applications. Currently he is working on time series prediction on irregular time series using deep learning architectures.