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If you wish to modify the initial email your leads receive then you will need to update the opt-in confirmation email:

Select your list SelectSignup Forms. Select General Forms. Open the Forms Response Emails Select Opt-in confirmation email. Edit the confirmation email

That's it! Your leads will now receive a customized confirmation email when signing up to your list.

There are a few things that could go wrong to indicate that your integration isn't working properly. Before you get in touch with us, please take a look at the following potential issues:

Check out the Field Mapping documentation if you haven't already, and make sure you've configured your MailChimp integration to send your form fields to the appropriate fields in your MC list.

If your MailChimp list has some fields marked "required" that we aren't passing through from your Unbounce form then the integration will fail. To check this, edit the form for the appropriate list in MailChimp and check to see which fields are required.

If it looks like Unbounce has captured the lead, but you don't see them in MailChimp (and the above things are non-issues) then it's possible your leads are not clicking on their MailChimp confirmation link that should be delivered in an Email. Try walking through the process with your own email and checking to see if the integration is working for you. You may want to check the content of your confirmation email in MailChimp and make sure the link is still prominent.

MailChimp does not accept international characters in the prefix of an email address. There is no way for Unbounce to force email addresses with international characters through to MailChimp. Unfortunately this is a block implemented by MailChimp.

Click to learn more about MailChimp's supported characters in email prefixes.

MailChimp : an email marketing service provider.

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Information sent from Unbounce to MailChimp : email, Page ID, Page Variant, Submission Date

Information sent from Unbounce to MailChimp

Autoresponder emails : enables you to automatically send emails to leads at set times

Autoresponder emails

Reasons why you would want to use an autoresponder : delivering an ebook, sending an ecourse automatically, offering a product demo or special offer, inviting people to download content

Tit Petric

Tit Petric is a senior software engineer and the author of API Foundations in Go . He's currently writing his second book, 12 Factor Apps with Docker and Go .

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Last updated: 2017-05-08

by Tit Petric | 0 Comments

Development

Reading Time: 10 minutes

This article was originally published on Scene-SI by Tit Petric . With their kind permission, we’re sharing it here for Codeship readers.

Scaling your service has usually been in the domain of system operators who installed servers and developers who tweaked software when the load got high enough to warrant scaling. Soon enough you’re looking at tens or even hundreds of instances that take a lot of time to manage.

After the release of Docker 1.12, you now have orchestration built in — you can scale to as many instances as your hosts can allow. And setting up a Docker swarm is easy-peasy.

“Scale to as many instances as your hosts can allow by setting up a Docker swarm.” via @TitPetric

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First off, I’m starting with a clean Docker 1.12.0 installation. I’ll be creating a swarm with a few simple steps:

I now have a swarm consisting of exactly one manager node. You can attach additional swarm workers or add new managers for high availability. If you’re running a swarm cluster with only one manager and several workers, you’re risking an interruption of service if the manager node fails.

“In Docker Swarm, the Swarm manager is responsible for the entire cluster and manages the resources of multiple Docker hosts at scale. If the Swarm manager dies, you must create a new one and deal with an interruption of service.”

As we’re interested in setting up a two-node swarm cluster, it makes sense to make both nodes in the swarm be managers. If one goes down, the other would take its place.

To list the nodes in the swarm, run docker node ls .

As you see, when adding a new manager node, it’s automatically added but not promoted to the leader. Let’s start some service that will perform something that can be scaled over both hosts. I will ping google.com , for example. I want to have five instances of this service available from the start by using the --replicas flag.

You may wonder why LSTMs have a forget gate when their purpose is to link distant occurrences to a final output. Well, sometimes it’s good to forget. If you’re analyzing a text corpus and come to the end of a document, for example, you may have no reason to believe that the next document has any relationship to it whatsoever, and therefore the memory cell should be set to zero before the net ingests the first element of the next document.

In the diagram below, you can see the gates at work, with straight lines representing closed gates, and blank circles representing open ones. The lines and circles running horizontal down the hidden layer are the forget gates.

It should be noted that while feedforward networks map one input to one output, recurrent nets can map one to many, as above (one image to many words in a caption), many to many (translation), or many to one (classifying a voice).

You may also wonder what the precise value is of input gates that protect a memory cell from new data coming in, and output gates that prevent it from affecting certain outputs of the RNN. You can think of LSTMs as allowing a neural network to operate on different scales of time at once.

Let’s take a human life, and imagine that we are receiving various streams of data about that life in a time series. Geolocation at each time step is pretty important for the next time step, so that scale of time is always open to the latest information.

Perhaps this human is a diligent citizen who votes every couple years. On democratic time, we would want to pay special attention to what they do around elections, before they return to making a living, and away from larger issues. We would not want to let the constant noise of geolocation affect our political analysis.

If this human is also a diligent daughter, then maybe we can construct a familial time that learns patterns in phone calls which take place regularly every Sunday and spike annually around the holidays. Little to do with political cycles or geolocation.

Other data is like that. Music is polyrhythmic. Text contains recurrent themes at varying intervals. Stock markets and economies experience jitters within longer waves. They operate simultaneously on different time scales that LSTMs can capture.

A gated recurrent unit (GRU) is basically an LSTM without an output gate, which therefore fully writes the contents from its memory cell to the larger net at each time step.

A commented example of a LSTM learning how to replicate Shakespearian drama, and implemented with Deeplearning4j, can be found here. The API is commented where it’s not self-explanatory. If you have questions, please join us on Gitter .

Here’s what the LSTM configuration looks like:

GET STARTED WITH DEEP LEARNING

Here are a few ideas to keep in mind when manually optimizing hyperparameters for RNNs:

1) While recurrent networks may seem like a far cry from general artificial intelligence, it’s our belief that intelligence, in fact, is probably dumber than we thought. That is, with a simple feedback loop to serve as memory, we have one of the basic ingredients of consciousness – a necessary but insufficient component. Others, not discussed above, might include additional variables that represent the network and its state, and a framework for decisionmaking logic based on interpretations of data. The latter, ideally, would be part of a larger problem-solving loop that rewards success and punishes failure, much like reinforcement learning. Come to think of it, DeepMind already built that

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