Unveiling the Hidden Layers of Neural Networks

Inside AI

A guide to visualizing the black-box of Deep Learning

Connections from one neuron to another in the brain’s cortex have inspired the creation of algorithms that mimic these intricate links. The learning approach of a neural network can be understood, for the sake of simplicity, to be analogous to the manner in which newborns learn to recognize their parents- by observing and listening to them over a period of time. Once a Neural Network trains on a considerable size of the required dataset, the network uses its knowledge- ‘weights’ to be precise — to identify or recognize patterns on an unknown data sample.

Let’s consider the task of identifying faces, the network would begin analyzing individual pixels of an image from the input layer. Following the input layer, the “hidden” layers iteratively learn to identify geometric shapes and features that are distinctive to a particular face like the eyes, lips, scars, etc that make up the entire image. In the final output layer, it makes a well-educated guess about whom the face belongs to, from the probability information it has calculated.

Evidently, these hidden layers play a vital role in eventually breaking down input into valuable information. Each layer from input until the output handles increasingly complex information, and it is often said that — the hidden layers — as their name suggests — are shrouded in mystery, but are they?

The US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. The researchers trained a neural network on two sets, one with 50 photos of camouflaged tanks amid trees, and the other with 50 photos of trees without tanks. The researchers then took…

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