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Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

In-Person Training
Neural networks for time series analysis using Deeplearning4j

Tom Hanlon (Functional Media)
9:00am–5:00pm
Sunday, April 29 through Monday, April 30
Location: Madison

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

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.

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

Hardware and/or installation requirements:

  • A laptop with an up-to-date browser installed

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.

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 instructor

Photo of Tom Hanlon

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.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package.

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Comments

Dwayne Bradley | TECHNOLOGY DEVELOPMENT MANAGER
04/28/2018 4:17pm EDT

Thanks Tom! That’s perfect. See you tomorrow morning.

Dwayne

Picture of Tom Hanlon
Tom Hanlon | SENIOR INSTRUCTOR
04/25/2018 2:54pm EDT

Dwayne,

I will have a Virtual Machine “OVA file” available on a usb drive, I think it is about 10G compressed.

Come with space on your laptop or a usb drive and you can have the Virtual machine.

I will also share the labs as a github repo.


Tom

Dwayne Bradley | TECHNOLOGY DEVELOPMENT MANAGER
04/25/2018 10:41am EDT

Will we be able to access the remote lab environment after the training is over? If not, I would still like to have the virtual machine so I can be able to reference it later.

Dwayne

Picture of Tom Hanlon
Tom Hanlon | SENIOR INSTRUCTOR
04/10/2018 12:14pm EDT

The Labs have been moved from a Virtual Machine to a remote Lab environment,

So Don Clavio, and others you will require a laptop with a modern browser. The 10G disk space and 10G of ram (or more) requirement reflects the old Virtual Machine requirement.

A modern machine with a modern browser will suffice.

Thanks

Picture of Majid Shaalan
Majid Shaalan | DIRECTOR OF COMPUTER & INFORMATION SCIENCES PROGRAM
04/08/2018 4:42pm EDT

I have the Platinum package and would love to attend. But, I won’t be able to as I will be in another one same time both days. Can get the training material/ resources for this?

Don Clavio | SR. SOFTWARE ENGINEER
03/15/2018 5:27am EDT

The TimeSeries Analysis Deep Learning class requires a laptop with 10 gb of space. Is that 10gb of RAM. Please confirm.