Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations.

01

Inspired by the human brain

Like the architecture of the brain, neural networks are layered networks of artificial “neurons”.

Numbers are taken as inputs and output a single number through a simple function.

02

How a neuron works

Like the architecture of the brain, neural networks are layered networks of artificial “neurons”. Numbers are taken as inputs and output a single number through a simple function. A neuron may have multiple inputs. Its output to the next neuron layer could be a sum of those inputs, or w…. Where i is the number of inputs.

03

Built for you,
trained on your data

Once we’ve trained the neural network, your historical data is pushed through it. And the neural network outputs the probability of profile.

04

Verification and back-propagation

The neural network observes the actual result. Through back-propagation, it adjusts the weights in the network to ensure its output is even more accurate for the next set of inputs.

05

Enhanced accuracy over time

Just like a human brain learns and hones skill with repetition, the weights in the neural network get more accurate over time.

The ability to improve and identify complex patterns is the most advanced breakthrough in machine learning.