What is a Neural Network?

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Get an introduction to the interesting space of neural networks in this article, which is a subfield of artificial intelligence (AI).


Neural networks are among the central technologies of artificial intelligence. They have been around for years without you knowing about it. Yes, you use it every time your smartphone uses facial recognition. It is also used in dozens of applications in business analytics, finance, and enterprise training. But they are growing even more prevalent with time.

They are delivering unprecedented processing power and new and exciting benefits every single day. But what is a neural network? And how are they changing the face of technology?

Such brain-simulating computer applications are easier to understand than you might think. Delve deeper into this article to understand and learn more about neural networks. 

Neural Network Definition

An artificial neural network is a system of hardware or software patterned after the working of human brain neurons and the nervous system. They are a variety of deep learning technology coming under the broad domain of artificial intelligence.

Deep learning is a branch of machine learning using several types of neural networks. These algorithms are inspired by the way a human brain functions. Therefore many experts believe that they are our best shot at moving toward real artificial intelligence.

How do neural networks work?

A neural network has a large number of processors that operate parallel but are arranged as tiers. Similar to how the human optic nerve receives the raw information, the first tier receives the raw input.

Then, each successive tier receives input from its preceding tier and passes its output to the tier after it. The final output is processed by the last tier.

Small nodes make up each tier and the nodes are interconnected with the preceding and succeeding nodes in the tier. Each node in the neural network has its sphere of knowledge. This includes the rules it was programmed with and rules it learns by itself.  

The key to the efficacy of neural networks is they learn very quickly and are extremely adaptive. Each node weighs the importance of the input received from the preceding nodes. The inputs contributing most towards the right output are given the highest weight.

Types of neural networks

1. Feedforward Neural Network

It is one of the simplest types of neural networks. Here, the data passes through the different input nodes till the time it reaches the output node. That means data only moves in one direction from the first tier until it reaches the output node.

2. Radial basis function neural network

A radial basis function takes the distance of a point relative to the center. Radial basis function neural networks have two layers. The features are combined with the radial basis function in the inner layer. The output of these features is considered when calculating the same output in the next time step.

3. Multilayer perceptron

A multilayer perceptron neural network has three or more layers and is used to classify the data that cannot be separated linearly. It is a type of neural network that is fully connected since every node in a layer is connected to each node in the following layer.

4. Convolutional neural network (CNN)

A convolutional neural network uses a variation of the multilayer perceptrons and contains one or more convolutional layers. These layers can be completely interconnected or pooled.

A convolutional layer uses a convolutional operation on the input before passing the result to the next layer. The network tends to be deeper but with much fewer parameters due to this convolutional operation.

5. Recurrent neural network (RNN)

A Recurrent Neural Network is a type of neural network in which the output of a specific layer is saved and fed back to the input to predict the outcome of the layer.

6. Modular neural network

A modular neural network contains different networks functioning independently and performing sub-tasks. These different networks do not signal or interact with each other during the computation process as each of them works independently towards achieving the output.

7. Sequence-to-sequence models

A sequence-to-sequence neural network model consists of two recurrent neural networks. One of them is an encoder that processes the input and the second one is a decoder that processes the output.

The encoder and decoder use the same or different parameters. This model is applicable in cases where the input data length is not the same as the output data length.  

8. Deep neural networks (DNN)

A deep neural network is an artificial neural network with multiple layers between the input and output layers. They always consist of the same components such as neurons, synapses, weights, biases, and functions.

9. Graph neural network (GNN)

Graph neural networks are a class of deep learning methods. They are designed to perform inference on data described by graphs. They are neural networks that can be directly applied to graphs. They provide an easy way to do edge-level, node-level, and graph-level prediction tasks. They can do what convolutional neural networks fail to do.


Neural networks are ridiculously good at generating results. However, they are also mysteriously complex. In fact, the complexity of the decision-making process makes it even more difficult to determine exactly how neural networks arrive at the superhuman level of accuracy.

If you need help developing projects on neural networks, we can help you out in the journey and relish in all its mystery!

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