2 Leman St, London E1 8FA, London
Deep learning is without a doubt among the hottest topics in data science today. Computers are now more powerful than ever, and as a result, deep learning has been applied successfully by academics during the past few years.
However, it is still unclear how difficult it is for businesses to apply it. We want to go beyond the buzzword and share concrete examples of where deep learning has been successfully used.
Join us to see how deep learning can be applied to recommender systems and how it can be deployed with or without programming experience.
This meet-up is supported by ODSC. Of course, there will beers and pizzas for everyone!
- "How to Improve your Recommender System with Deep Learning: A Use Case" from Pierre Gutierrez and Alexandre Hubert, Dataiku data scientists
- "H20 Deep Water. Making Deep Learning Accessible to Everyone," from Jo-Fai, H2O.ai data scientist
Recommender systems are paramount for e-business companies. There is an increasing need to take into account all user information to provide the best, most tailored products. One important element is the content that the user actually sees: the visual of the product.
In this talk, we will describe how Dataiku improved an e-business vacation retailer recommender system using the content of images. We’ll explain how to leverage open datasets and pre-trained deep learning models to derive user preference information. This transfer learning approach enables companies to use state-of-the art machine learning methods without having deep learning expertise.
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, I will go through the motivation and benefits of Deep Water. After that, I will demonstrate how to build and deploy deep learning models with or without programming experience using H2O's R/Python/Flow (Web) interfaces.