neural network its types and how does it works

What is a neural network in AI? its types? how does it works?

What is a neural network in AI?

Answer to the question, Neural Networks in Artificial Intelligence(AI) is like a part of a computing system designed to imitate the way the human brain analyzes and processes information that asked to solve problems that are difficult for human standards.

How neural network of the human brain similar to computers

 As a computer runs with help of program code information in form of binary bits, that we do to accomplish a task similarly a typical human brain contains something like 50 billion minuscule cells called neurons (may be from about 20 billion to as many as 500 billion if we estimate).

neuron is just a node with many input segments and one output. A neural network consists of many interconnected neurons. In fact, it is a “simple” device that receives data at the input and provides a response.

Each neuron is composed up of a cell body (the central mass of the cell) with a number of connections coming from it, infinite dendrites (inputs – carrying information toward the cell body), and a single axon (output – carrying information away).

These densely interconnected brain cells inside a computer can get you to learn the material objects, recognize patterns, and make decisions in a human-like way. The best part about a neural network is that you don’t have to program it to learn explicitly.

Types of Neural Networks

  1. Modular Neural Network
  2. Convolutional Neural Network
  3. Multilayer Perceptron
  4. Feed Forward Neural Network
  5. Radial Basis Function Neural Network
  6. Recurrent Neural Network(RNN)
  7. Long Short Term Memory(LSTM)
  8. Sequence-To-Sequence Models

As above we have seen What is Neural Network in AI and its types and now will continue with How does Neural Network Works actually.

How does Neural Network Works

Once the system builts the network has been trained with some well enough learning examples, that led to feed numerous inputs and see how it responds back.

For example, to make a topic guide a network by showing it lots of animative features of objects representing some appropriate way it can understand, and make him to recognize the objects perfectly. After showing it, you can feed a picture of some new design its not encountered before – let’s say a sofa as chair like shape and see what happens.

Depending on how you’ve trained the network, it will atempt to recongnize things as part of different objects taken to explain it just like human.

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