Apache Big Data Seville 2016 – Deep Neural Network Regression at Scale in Spark MLlib – Jeremy Nixon

Deep Neural Network Regression at Scale in Spark MLlib – Jeremy Nixon

Deep Neural Network Regression at scale in Spark MLlib – Jeremy Nixon will focus on the engineering and applications of a new algorithm in MLlib. The presentation will focus on the methods the algorithm uses to automatically generate features to capture nonlinear structure in data, as well as the process by which it’s trained. Major aspects of that are the compositional transformations over the data, advantages of the various activation functions, the final linear layer, the cost function and training via backpropagation. Applications will look into how to use neural network regression to model data in computer vision, finance, and the environment. Details around optimal preprocessing, the type of structure that can be found, and managing its ability to generalize will inform developers looking to apply nonlinear modeling tools to problems that they face.

More information about this talk

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s