What is a Neural network?

Types of neural networks

Neural networks come in various flavors. It is also possible to divide main categories into subcategories. Here is a list of the most commonly used neural networks.

Feedforward neural network: A feed-forward neural network is an artificial neural network with no loops between the units. There is only one direction in which the information moves in this network: forward. That is, from input nodes through hidden nodes, if any, and to output groups. This type of network does not contain loops or cycles. Feedforward neural networks can be divided into two types:

• Single layer perceptron: It is the simplest type of neural network without hidden layers. This network is made up of only one layer. In other words, it is the output layer. When counting the layers of a network, we do not include the input layer since no calculations are performed in the input layer. Several weights connect the inputs directly to the outputs; Thus, this network is called a single-layer perceptron.
• Multi-layer Perceptron (MLP): A feed-forward network consists of several layers of computing units connected to one another in a feed-forward pattern. Neurons in one layer are directly connected to neurons in the next. Multilayer perceptrons are very useful because they can learn non-linear descriptions (in many cases, the data we deal with is not linearly separable).

Convolutional neural network: A convolutional neural network is similar to a regular neural network. They also consist of neurons with trainable weights and biases. In convolutional neural networks (CNNs or ConvNets), the connection pattern of units is inspired by the visual cortex. An area of space called the receptive field allows units to respond to stimuli. Each receptive field partially overlaps the other and covers the entire field of vision. The convolution operator can mathematically approximate the response of each unit. In image and video recognition, recommender systems are the main applications of these networks. A large amount of data is required to train convolutional neural networks.

Recurrent neural networks: In Recurrent Neural Networks (RNN), the connections between units form a directed loop (these networks propagate data forward and backward, from the final processing levels to the initial levels). A temporary dynamic behavior can be seen in this structure. In contrast to feed-forward neural networks, recurrent neural networks can process arbitrary input sequences. As a result, these networks can perform tasks such as handwriting recognition, voice recognition, and time series estimation.