What is Artificial Neural Network !?
Artificial neural networks, also known as neural networks (NNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms.
Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have artificial neurons that are interconnected to one another in various layers of the networks.
Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
Thus the binary data obtained is compared with the actual output. If it differs, it adjusts its results until it delivers an output that coincides with actual output.
The architecture of an Artificial Neural Network
Artificial neural networks (ANNs) are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer.
Input Layer:
As the name suggests, it accepts inputs in several different formats provided by the programmer.
Hidden Layer:
The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.
It takes the input and computes the weighted sum of the inputs and includes a bias. This computation is represented in the form of a transfer function.
It determines weighted total is passed as an input to an activation function to produce the output. Activation functions choose whether a node should fire or not. Only those who are fired make it to the output layer.
Output Layer:
An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually. During this supervised phase, the network compares its actual output produced with what it was meant to produce — the desired output.
The difference between both outcomes is adjusted using backpropagation. This means that the network works backward, going from the output unit to the input units to adjust the weight of its connections between the units until the difference between the actual and desired outcome produces the lowest possible error.
Advantages of Neural Networks
- A neural network can perform tasks that a linear program cannot.
- When an element of the neural network fails, its parallel nature can continue without any problem.
- A neural network learns and reprogramming is not necessary.
- It can be implemented in any application.
Just as a human brain differentiates between cats and dogs, and different human faces, we can train the machine with neural network algorithms, to perform these similar functions, which is mind-blowing, if you ask me!
Artificial Neural Network, by mimicking the most complex organ of the human body, has opened a portal of new possibilities.
Hope you had fun learning something new and keep exploring!
