Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

Neural networks for time series analysis using Deeplearning4j (Day 2)

Tom Hanlon (Functional Media)
9:00am–5:10pm Monday, April 30, 2018
Location: Madison

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Tom Hanlon demonstrates how to use Deeplearning4j to build recurrent neural networks for time series data.


Day 1

  • Introduction to neural networks and an overview of the different types of neural networks (feed forward, convolutional, and recurrent)
  • Choosing the appropriate neural network for time series data
  • Hands-on lab: Generating weather forecasts with a recurrent neural network

Day 2

  • Configuring a data ingestion pipeline sequence data
  • Recurrent neural nets for the prediction of medical outcomes
  • Recurrent neural nets for the classification of sequence data
  • Hands-on lab: Sequence classification with recurrent neural networks
Photo of Tom Hanlon

Tom Hanlon

Functional Media

Tom Hanlon is a senior instructor at Functional Media, where he delivers courses on the wonders of the Hadoop ecosystem. Before beginning his relationship with Hadoop and large distributed data, he had a happy and lengthy relationship with MySQL with a focus on web operations. He has been a trainer for MySQL, Sun, and Percona.