Deep Learning
This lesson discusses deep learning.
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Introduction
For decades researchers have been trying to deconstruct the inner workings of our incredible and fascinating brains, hoping to learn to infuse a brain-like intelligence into machines. For example, when we were toddlers, we did not learn to recognize objects by learning their distinctive features. A child learns to call a cat “a cat” and a dog “a dog” by being exposed to the same example many times and by being corrected for the wrong guesses. This extremely active, “inspired from the brain” field of artificial computer intelligence is called deep learning. The corresponding programming paradigm, which allows computers to learn from data, is called artificial neural networks (ANN).
Just like a biological neuron has dendrites to receive signals, a cell body to process them, and an axon to output the signals to other neurons, the artificial neuron (also referred as a perceptron) has a number of input channels, a processing unit, and an output channel that can send signals to multiple other nodes.
Each input (x0, x1, x2) to the neuron has an associated weight (w0, w1, w2), which is assigned on the basis of its relative importance to other inputs. Each of the inputs is multiplied by its weight, and then the processing unit applies a transformation function f, called the activation function, to the weighted sum of its inputs, as shown in the figure below.
The ANN additionally takes another input 1 with weight b (called bias, shown in the ...
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