Deep Learning Algorithms in eCommerce

What is deep learning?Deep learning is a branch of machine learning which has been developed to help us discover and trace user behaviour online at a more complex level than ordinary machine learning.

Other names for deep learning include deep structured learning, hierarchical learning or deep machine learning.

Deep learning is based on a set of algorithms that try to mimic high level abstractions in data.

How does deep learning work?Although deep learning is a highly complicated and technical process, it can be summed up with a comparison to the human brain.

“Deep learning algorithms transform their inputs through more layers than shallow learning algorithms. At each layer, the signal is transformed by a processing unit, like an artificial neuron, whose parameters are ‘learned’ through training. A chain of transformations from input to output is a credit assignment path. CAPs describe potentially causal connections between input and output and may vary in length.”

This data has allowed deep learning algorithms to trace the buyer journey and by doing that we now have a fairly clear picture of what kind of product information buyers search for when they are making purchase decisions for different things.

Fortunately for Mary the site uses deep learning algorithms.

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Black-Box Algorithms: Ready For Medical Use? : Shots

Black-Box Algorithms: Ready For Medical Use? : Shots – Health News It’s hard for humans to check algorithms that computers devise on their own.

Zech and his medical school colleagues discovered that the Stanford algorithm to diagnose disease from X-rays sometimes “Cheated.” Instead of just scoring the image for medically important details, it considered other elements of the scan, including information from around the edge of the image that showed the type of machine that took the X-ray.

Black-box algorithms are the favored approach to this new combination of medicine and computers, but “It’s not clear you really need a black box for any of it,” says Cynthia Rudin, a computer scientist at Duke University.

She is pushing back against a trend in the field, which is to add an “Explanation model” algorithm that runs alongside the black-box algorithm to provide clues about what the black box is doing.

Shah developed an algorithm that could scan medical records for people who had just been admitted to the hospital, to identify those most likely to die soon.

It is equally important to avoid misuse of an algorithm, for example if a health insurer tried to use Shah’s death-forecasting algorithm to make decisions about whether to pay for medical care.

“I firmly believe that we should be thinking about algorithms differently,” Shah says.

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