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.
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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.
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Comments
Thanks Tom! That’s perfect. See you tomorrow morning.
Dwayne
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.
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Tom
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
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
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?
The TimeSeries Analysis Deep Learning class requires a laptop with 10 gb of space. Is that 10gb of RAM. Please confirm.