Neural networks are a way of attempting to simulate the brain electronically. We know that our brain is made up of about 100 billion tiny units called neurons. Each neuron is connected to thousands of other neurons and communicates with them through electrochemical signals. Traditionally, the term neural network was used to refer to a network or circuit of biological neurons. But in modern times, the term is also used for artificial neural networks, which are made up of artificial neurons or nodes.
Neural networks are a set of algorithms used to find fundamental relationships in sets of data through a process that mimics the human brain. Neural networks facilitate pattern recognition and solving common problems in the fields of artificial intelligence, machine learning, and deep learning.
How Many Types Of Neural Networks Are There?
Neural networks can be classified into different types, which are used for different purposes. Some main types of neural networks are given below:-
- Multilayer Perceptron
- Feedforward Neural Networks
- Convolutional Neural Network
- Recursive Neural Network
- Recurrent Neural Network
- Long/ short term memory
What Are The Characteristics Of Neural Network?
The main features of neural network are as follows:-
- Expert System
- Adaptive Learning
- self organization
- Real Time Operation
- Fault Tolerance via Redundant Information Coding
- Capability of deriving the meaning from complicated data
What Skills Required For Neural Network?
- Fundamental of programming skills
- Software engineering and system design
- Knowledge of applied mathematics and algorithms
- Probability and statistics
- Distributed computing
- Data modeling and evaluation
How Neural Network Works?
Once the network is trained with enough learning examples, it reaches the point where you can present it with a completely new set of inputs.
There are many layers in a neural network. Each layer performs a specific function, and the more complex the network, the more layers there are. Therefore, neural network is also called multi-layer percepton. There are three main layers in a neural network.
Conclusion
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