Preceptron — The foundation stone of the Modern AI

Preceptron — The foundation stone of the Modern AI

To understand this, let's move a little off-topic. We do know that most human inventions, discoveries, or innovations are inspired by nature, for example, flights, inventors analyzed birds’ flight with a desire to fly high in the sky like them. Studying the whales, scientists came up with an idea to invent the submarine. The invention of the sonar system was inspired by bats and dolphins that use echolocation for navigation.

What is the big buzz about machine learning, deep learning, AI, and stuff?

To understand this, let's move a little off-topic.

We do know that most human inventions, discoveries, or innovations are inspired by nature, for example, flights, inventors analyzed birds’ flight with a desire to fly high in the sky like them. Studying the whales, scientists came up with an idea to invent the submarine. The invention of the sonar system was inspired by bats and dolphins that use echolocation for navigation.

Similarly, deep learning is inspired by the human brain. Just as how the human brain learns through trial and error i.e., gaining knowledge by studying, experience, making mistakes, or being taught, deep learning deals with algorithms that aid machines with intelligence without explicit programming.

The Biological Neuron

Let’s get to know about the biological neuron before diving into the perceptron.

  • The dendrites are responsible for receiving the information from other neurons it is connected to. The dendrites connect with other neurons through a gap called the synapse that assigns a weight to a particular input.
  • The *Soma *is the cell body of the neuron and is responsible for the processing of information that is received.
  • The** Axon** is just like a cable through which the neuron sends the output to the axon terminals. These axon terminals are connected to the dendrites of other neurons through the synapse.

So to put it all together, the neuron takes some binary inputs through the dendrites, but not all inputs are treated the same since they are weighted. If the combination of these inputs exceeds a certain threshold, then an output signal is produced, i.e., the neuron “fires.” but if the combination falls short of the threshold, then the neuron doesn’t produce any output, i.e., the neuron “doesn’t fire”. When the neuron fires, this single output travels along the axon to other neurons.

deep-learning machine-learning preceptron ai

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