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Definition: A computer program is said to learn from experience E with respect to some class of tasks T and perform-ance measure P, if its performance at tasks in T, as measured by P, improves with experience E. [Mitchell 97]
Example: T = “play tennis”, E = “playing matches”, P = “score”
A good example is the processing of visual information: a one-year-old baby is much better and faster at recognising objects, faces, and other visual features than even the most advanced AI system running on the fastest super computer.
Most impressive of all, the brain learns (without any explicit instructions) to create the internal representations that make these skills possible
The brain is composed of approximately 100 billion (10 11 ) neurons
Schematic drawing of two biological neurons connected by synapses A typical neuron collects signals from other neurons through a host of fine structures called dendrites . T he neuron sends out spikes of electrical activity through a long, thin strand known as an axon , which splits into thousands of branches. A t the end of the branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. W hen a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on the other changes
A neural net simulates some of the learning functions of the human brain. It can recognize patterns and "learn." You can use it to forecast and make smarter business decisions. It can also serve as an "expert system" that simulates the thinking of an expert and can offer advice. Unlike conventional rule-based artificial-intelligence software, a neural net extracts expertise from data automatically - no rules are required.
In other words through the use of a trial and error method the system “learns” to become an “expert” in the field the user gives it to study.
The activation signal is passed through a transform function to produce the output of the neuron, given by
The transform function can be linear , or non-linear , such as a threshold or sigmoid function [more later …].
For a linear function, the output y is proportional to the activation signal a . For a threshold function, the output y is set at one of two levels, depending on whether the activation signal a is greater than or less than some threshold value. For a sigmoid function, the output y varies continuously as the activation signal a changes.
Artificial neural network models (or simply neural networks) are typically composed of interconnected units or artificial neurons. How the neurons are connected depends on some specific task that the neural network performs.
Two key features of neural networks distinguish them from any other sort of computing developed to date:
Neural networks are adaptive, or trainable
Neural networks are naturally massively parallel
These features suggest the potential for neural network systems capable of learning, autonomously improving their own performance, adapting automatically to changing environments, being able to make decisions at high speed and being fault tolerant.