Here’s an interesting write up on Deep Learning from Forbes, that provides an overview of the technology for non-practitioners.

“During training, you define the number of neurons and layers your neural network will be comprised of and expose it to labeled training data,” said Brian Cha, who is a Product Manager and Deep Learning evangelist at FLIR Systems. “With this data, the neural network learns on its own what is ‘good’ or ‘bad.’ For example, if you want the neural network to grade fruits, you would show it images of fruits labeled ‘Grade A,’ ‘Grade B,’ ‘Grade C,’ and so on.

The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on. You don’t need to manually define these characteristics or even program what is too big or too small, the neural network trains itself using the training data. The process of evaluating new images using a neural network to make decisions on is called inference. When you present the trained neural network with a new image, it will provide an inference, such as ‘Grade A with 95% confidence.’”

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