Deep Learning for Biotechnology on Qubole

Presented by

Matt Der, Chief Technology Officer,

About this talk

In the biological sciences, hypothesis-driven experiments and bottom-up design experiments rely on predicting what will happen with new cells and molecules. Machine learning excels at prediction and has become more democratized, making it an important component in the biotech toolkit. We use Merck's Kaggle competition as a representative task in this domain that involves predicting molecular activity from numeric descriptors of chemical structure. Our approach utilizes deep neural networks using the Keras library in a Qubole notebook, which is conveniently attached to an autoscaled Spark cluster. We use Spark to distribute the hyperparameter search for optimizing the neural net.

Related topics:

More from this channel

Upcoming talks (0)
On-demand talks (118)
Subscribers (8244)
Tune in to hear from open data lake platform leaders and engineers discuss everything from continuous date engineering on data lakes for machine learning, streaming analytics, ad-hoc analytics and data exploration in the cloud. The interactive talks are designed for both data engineers, data analysts and data scientists that want to learn about some of the challenges and solutions for use cases seen in data-driven organizations. Learn more about Qubole: