4 Types of Neural Networks to Know About

1. 4 Types of Neural 4 Types of Neural Networks to Networks to Know About Know About There are several different types of neural networks that are used for different purposes. Some topologies are going to be covered here. 1-Single-Layer Neural Network There are two layers in it. First is the input layer, and the other is the output layer. It is a simple neural network in which there are no hidden layers. It takes an input and then calculates the weighted input for each node. Then it makes use of a sigmoid function for

2. classification. It is used for classification, encode database and also monitor access data. 2-Feed Forward It is an artificial neural network in which the nodes don’t form a cycle. In this neural network, all the perceptron is arranged in different layers. The input layer takes the input, and the output layer generates the output. The hidden layer doesn’t have any connection with the outer world, as their name suggests. In this type of neural network, every receptor in one layer is connected with each node in the next layer. Hence, all the nodes are fully connected. Another point to note is that there is no visible or invisible connection between the nodes in the same layer. There are no back- loops in the feed-forward network. So, to minimize the prediction error, the back-propagation algorithm is used. It is used for data compression, recognition of patterns, computer vision, target recognition, and speech recognition. Handwritten character recognition also makes use of this algorithm. 3-Radian Basis Network These are mainly used for the function approximation issues. These can be distinguished from the other neural networks due to the quick learning rate. The primary difference between the radial basis networks and the feed-forward one is that the former uses a radial basis function for the activation function. A sigmoid function gives an output between 0 and 1 to find whether the answer is yes or no. If there are continuous values, the RBN can’t be used. It determines

3. how far the generated output is from the targeted output. It can be useful in the case of continuous values. These have found their usage in function approximation, time series prediction, classification, and system control. 4-Deep Feed Forward It is a feed-forward network that uses more than one hidden layer. By adding more hidden layers, reduced overfitting and improved generalization can be achieved. It is used for data compression, pattern recognition, computer vision, and financial prediction. Simple neural network has changed to into complex and they are used in different ways too.