Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

In-Person Training
Neural networks for time series analysis using Deeplearning4J

Josh Patterson (Skymind), Susan Eraly (Skymind), Tom Hanlon (Skymind)
Monday, June 26 & Tuesday, June 27, 9:00am - 5:00pm
Location: Gibson
Secondary topics:  Deep Learning, Health care, IoT and its applications
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Early Price ends May 12

This course will sell out—sign up today!

Participants should plan to attend both days of this 2-day training course. Training passes do not include access to tutorials on Tuesday.

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? Josh Patterson and Susan Eraly demonstrate how to use Deeplearning4J to build recurrent neural networks for time series data.

What you'll learn, and how you can apply it

  • Learn how to configure your dataset properly for use by the neural network, set appropriate hyperparameters for the neural network, configure your output layer for prediction and classification, and choose appropriate hardware

Prerequisites:

  • A working knowledge of at least one programming language (The labs will be performed in Java on a provided virtual machine.)

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? Josh Patterson and Susan Eraly demonstrate how to use Deeplearning4J to build recurrent neural networks for time series data.

Outline

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

About your instructors

Josh Patterson is the director of field engineering for Skymind. Previously, Josh ran a big data consultancy, worked as a principal solutions architect at Cloudera, and was an engineer at the Tennessee Valley Authority, where he was responsible for bringing Hadoop into the smart grid during his involvement in the openPDC project. Josh is a graduate of the University of Tennessee at Chattanooga with a master of computer science, where he did research in mesh networks and social insect swarm algorithms. Josh is a cofounder of the DL4J open source deep learning project and is a coauthor on the upcoming O’Reilly title Deep Learning: A Practitioner’s Approach. Josh has over 15 years’ experience in software development and continues to contribute to projects such as DL4J, Canova, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.

Susan Eraly is a software engineer at Skymind, where she contributes to Deeplearning4j. Previously, Susan worked as a senior ASIC engineer at NVIDIA and as a data scientist in residence at Galvanize.

Twitter for susan_eraly

Tom Hanlon is currently an instructor at Cloudera 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 , Percona.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. Early Price ends May 12.

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Comments

Alaa Emad |
03/20/2017 4:16am EDT

is there a financial aid for attending this workshop?