programming-for-dummies

654
Machine Learning
A neural network models itself on the human brain, which is divided into a
network of interlocking neurons. Each neuron acts like a primitive computer
that can receive input and produce output, which gets fed into another
neuron as input.
Although a single neuron may not be capable of doing much, a network of
interconnected neurons acts like a group of tiny computers that can tackle
different parts of a problem simultaneously, which is known in computer
science as parallel processing. Ordinary computers represent a single, fast
machine that can tackle a problem in sequential steps. The strength of a computer
is that it can perform these multiple steps much faster than a human
can, which is what makes computers so powerful.
Human brains can’t calculate as fast as a computer, but they can process multiple
data simultaneously. That makes human brains better at solving seemingly
simple problems, like recognizing a face from a crowd. On the other
hand, computers have trouble recognizing faces because computers try to
follow a sequential series of steps. As a result, computers are slow at recognizing
faces while a human’s ability to recognize faces is nearly instantaneous.
In a neural network, each neuron can accept data. The data is weighted by a
specific value. This total value is then compared to the neuron’s threshold. If
this value is less than the threshold, the neuron produces an output of zero
(0). If this value is greater than the threshold, the neuron produces an
output of one (1), as shown in Figure 4-3.
Figure 4-3:
How a
neuron
processes
and outputs
a value.
1
0
Threshold
0.7
1.25
0.6
Weights
0
The neuron in Figure 4-3 receives an input of 1 and 0. The 1 value is
weighted by 0.7 while the 0 value is weighted by 0.6, such as:
(1)*(0.7) + (0)*(0.6) = 0.7.
Because this value is less than the threshold value of 1.25, the neuron outputs
a value of 0.