WEEK 2

REVIEW OF ARTIFICIAL NEURAL NETWORKS

Artificial neural networks (ANN) hold the key to new technologies like driverless cars and voice control. It stems from bio-inspired computing. ANN is an information processing model that is inspired by the way the biological nervous system such as the brain processes information. It is composed of highly interconnected neurons that are processing elements. These elements work together to solve a specific problem.

The general model of an ANN is called a perceptron. Perceptron was introduced by Frank Rosenblatt in 1957. It has inputs of different weights, a bias value, and an activation function. This function converts the inputs of a node to an output signal which is used as an input to the next neuron in the system. The perceptron is active if the output is 1. The activation function applies a step rule (convert the numerical output into +1 or -1) to check if the output of the weighting function is greater than zero or not.

Perceptrons are building blocks that artificial neural networks use to learn (Deep learning). Perceptrons can also be used to construct logic gates.

References

  1. https://medium.com/towards-artificial-intelligence/building-neural-networks-from-scratch-with-python-code-and-math-in-detail-i-536fae5d7bbf
  2. https://medium.com/analytics-steps/understanding-the-perceptron-model-in-a-neural-network-2b3737ed70a2
  3. https://www.simplilearn.com/what-is-perceptron-tutorial
  4. https://en.wikipedia.org/wiki/Biological_neuron_model