Neural Network is a kind of interconnections systems, just like biological neural network brains in living beings. And neural network in artificial intelligence is just like performing the crucial tasks like humans developed through deep learning and machine learning.
In Artificial Neural Networks (ANN) the paths between the neurons is tuned by the learning algorithms. Human brains are connected with 86 billion nerve cells called neurons. While ANN is composed with multiple nodes which reproduce biological neurons of human brain.
And these artificial neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value.
Types of Artificial Neural Network
There are basically two types of Artificial Neural Network topologies − FeedForward and Feedback. And while developing the ANN model, all modes of machine learning training – supervised learning, unsupervised learning and reinforcement learning can be used as per the training data availability.
And to train the ANN based AI model, huge amount of training data sets are required. The training used to train the neural network model, must be labeled or annotated properly to make it usable in deep learning. And there are many companies providing the training data for machine learning, deep learning and artificial neural network models.