Artificial Neural Networks
,▪ A neural network can be understood as a network of hidden layers, an inpu
and an output layer that tries to mimic the working of a human brain. The
layers can be visualized as an abstract representation of the input data itself
▪ These layers help the neural network understand various features of the da
the help of its own internal logic.
▪ A neural network is a mathematical model that helps in processing informa
is not a set of lines of code, but a model or a system that helps proc
inputs/information and gives result.
▪ The information is processed in the simplest form over basic elements kn
‘neurons’. Neurons are connected and help exchange signals/information b
them with the help of connection links.
▪ This connection links between neurons could be strong or weak, and this s
of the connection links determines the method in which information is proc
▪ Every neuron has an internal state which can be determined by the in
connections from other neurons.
▪ Every neuron has an activation function which is calculated on its state, a
helps determine its output signal.
,A neural network can be understood as a computational gra
mathematical operations.
Two main characteristics of a neural network −
• Architecture
• Learning
Architecture:
It tells about the connection type: whether it is feedforward, recurrent
layered, convolutional, or single layered. It also tells about the num
layers and the number of neurons in every layer.
Learning :
It tells about the method in which the neural network is trained. A co
way to train a neural network is to use gradient descent and backpropag
, • What is Artificial Neural Network?
The term "Artificial Neural Network" is derived from Biological neural n
that develop the structure of a human brain. Similar to the human br
has neurons interconnected to one another, artificial neural networks al
neurons that are interconnected to one another in various layers
networks. These neurons are known as nodes. The given figure illustra
typical diagram of Biological Neural Network.
,▪ A neural network can be understood as a network of hidden layers, an inpu
and an output layer that tries to mimic the working of a human brain. The
layers can be visualized as an abstract representation of the input data itself
▪ These layers help the neural network understand various features of the da
the help of its own internal logic.
▪ A neural network is a mathematical model that helps in processing informa
is not a set of lines of code, but a model or a system that helps proc
inputs/information and gives result.
▪ The information is processed in the simplest form over basic elements kn
‘neurons’. Neurons are connected and help exchange signals/information b
them with the help of connection links.
▪ This connection links between neurons could be strong or weak, and this s
of the connection links determines the method in which information is proc
▪ Every neuron has an internal state which can be determined by the in
connections from other neurons.
▪ Every neuron has an activation function which is calculated on its state, a
helps determine its output signal.
,A neural network can be understood as a computational gra
mathematical operations.
Two main characteristics of a neural network −
• Architecture
• Learning
Architecture:
It tells about the connection type: whether it is feedforward, recurrent
layered, convolutional, or single layered. It also tells about the num
layers and the number of neurons in every layer.
Learning :
It tells about the method in which the neural network is trained. A co
way to train a neural network is to use gradient descent and backpropag
, • What is Artificial Neural Network?
The term "Artificial Neural Network" is derived from Biological neural n
that develop the structure of a human brain. Similar to the human br
has neurons interconnected to one another, artificial neural networks al
neurons that are interconnected to one another in various layers
networks. These neurons are known as nodes. The given figure illustra
typical diagram of Biological Neural Network.
