Deep learning sometimes seems like sorcery. Its state-of-the-art applications are at times delightful and at times disturbing. It’s no wonder that companies are eager to apply deep learning for more prosaic business problems like better churn prediction, image curation, chatbots, time series analysis and more. This talk won’t examine how to tune a deep learning architecture for accuracy. This talk will instead walk through basic steps to avoid common performance pitfalls in training, and then the right steps, in order, to scale up by applying more complex tooling and more hardware. Hopefully, you will find your modeling job can move along much faster without reaching immediately for a cluster of extra GPUs.
Sean is a data scientist at Databricks. He is an Apache Spark committer and PMC member, and co-author Advanced Analytics with Spark. Previously, he was director of Data Science at Cloudera and an engineer at Google.