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Start with fat-free design.

There’s a downside to having so many cooks in the design-led kitchen: you can get lost in the emotional craft of animations and high fives. Your polar star should always be the mindset of the user. “The goal is to get rid of everything that doesn’t add to what the user is trying to do,” says Lee. Trim the fat of storytelling that doesn’t relate to the ultimate goal. You can always go back and add more high fives later.

MailChimp’s design process begins with sketching ideas by hand. This is what resulted when MailChimp added the ability to create Facebook Ads through MailChimp. The purest storytelling icon–the slingshot in the top left corner–was chosen from the brainstorm. Image c/o MailChimp Design.

Connect the dots.

To lead like a designer, strategize around the customer experience (CX) versus the user experience UX). “When we talk about business, I lean on that CX layer of connecting the dots,” says Lee. Instead of zeroing in on UX (how customers use the product) and then layering customer support and marketing on top, bring designers and engineers in product, marketing, and customer service under one roof. The result blends operation and execution to create a unified focus on the experience. When designers aren’t siloed into different parts of the product journey, it’s easier to spot where a customer needs an elegant tech solution and where they need a high five.

Anticipate is the future.

Lee sees the future of design landing at one intersection: data and AI. Those two factors will come together in one key offering: anticipation. “The days of just showing data are over; it’s too static,” says Lee. Right now, most companies behave like Google Maps when the app tells you it will take you 20 minutes to get home. Future-oriented companies will combine that data intelligence with AI customization to anticipate what your needs will be when you get there. In the same way, designers need to embrace the push of tech to incorporate data and AI to create experiences that anticipate, versus respond to, customer needs.

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If we want people to read more books, we have to bring more books to the people—and their screens.

The answers included using waste artichoke and bio resin, carpentry made out of waste teak root, rainbow buildings, and more.

Because this feedback loop occurs at every time step in the series, each hidden state contains traces not only of the previous hidden state, but also of all those that preceded h_t-1 for as long as memory can persist.

Given a series of letters, a recurrent will use the first character to help determine its perception of the second character, such that an initial q might lead it to infer that the next letter will be u , while an initial t might lead it to infer that the next letter will be h .

Since recurrent nets span time, they are probably best illustrated with animation (the first vertical line of nodes to appear can be thought of as a feedforward network, which becomes recurrent as it unfurls over time).

In the diagram above , each x is an input example, w is the weights that filter inputs, a is the activation of the hidden layer (a combination of weighted input and the previous hidden state), and b is the output of the hidden layer after it has been transformed, or squashed, using a rectified linear or sigmoid unit.

Remember, the purpose of recurrent nets is to accurately classify sequential input. We rely on the backpropagation of error and gradient descent to do so.

Backpropagation in feedforward networks moves backward from the final error through the outputs, weights and inputs of each hidden layer, assigning those weights responsibility for a portion of the error by calculating their partial derivatives – ∂E/∂w , or the relationship between their rates of change. Those derivatives are then used by our learning rule, gradient descent, to adjust the weights up or down, whichever direction decreases error.

Recurrent networks rely on an extension of backpropagation called backpropagation through time , or BPTT. Time, in this case, is simply expressed by a well-defined, ordered series of calculations linking one time step to the next, which is all backpropagation needs to work.

Neural networks, whether they are recurrent or not, are simply nested composite functions like f(g(h(x))) . Adding a time element only extends the series of functions for which we calculate derivatives with the chain rule.

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is an approximation of full BPTT that is preferred for long sequences, since full BPTT’s forward/backward cost per parameter update becomes very high over many time steps. The downside is that the gradient can only flow back so far due to that truncation, so the network can’t learn dependencies that are as long as in full BPTT.

Like most neural networks, recurrent nets are old. By the early 1990s, the vanishing gradient problem emerged as a major obstacle to recurrent net performance.

Just as a straight line expresses a change in x alongside a change in y, the gradient expresses the change in all weights with regard to the change in error. If we can’t know the gradient, we can’t adjust the weights in a direction that will decrease error, and our network ceases to learn.

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