Types of Artificial Neural Networks in Machine Learning

Introduction 

The development of the worldwide neural networks market is anticipated to be fueled by significant progress in Artificial Intelligence (AI), a spike in cloud disruption in contemporary business, and the introduction of cutting-edge analytical tools and prediction solutions. On the other side, a scarcity of qualified specialists somewhat impedes progress. However, the industry is anticipated to benefit greatly from the increase in digitization and global internet usage. 

The international neural network market, which was anticipated to be worth $14.35 billion in 2020, is predicted to grow to $152.61 billion by 2030, exhibiting a CAGR of 26.7% from 2021 to 2030, according to a study issued by Allied Market Research. 

This blog is specifically written to help you understand the many kinds of neural networks that are frequently used, how they function, and how they are employed in the workplace. We’ll start this blog with a quick explanation of how neural networks function, and it has been kept as straightforward and efficient as possible. 

What Is Artificial Neural Network?  

Computers are unable to comprehend the context of real-world consequences the way that human brains do. Neural networks in artificial intelligence were initially created in the 1950s to solve this problem. Artificial Neural Networks (ANNs) are an attempt to replicate the network of neurons that build the human brain. It’s to make the computer learn things and make decisions in a way similar to that of a human. Creating ANNs in Machine Learning involves standard programming computers to act like networked brain cells. A few examples of Artificial Neural Networks’ applications are as follows:   

• Manufacturing robots  
• Self-driving cars 
• Smart assistants 
• Healthcare management 
• Automated financial investing 

An artificial neural network has three or more interconnected layers. The first layer is made up of neurons in the input layer, and these neurons send data to deeper layers, which then pass the data along to the ultimate output layer. 

Information transferred from layer to layer is adaptively transformed by the units that make up the inner layers, which are all hidden. The ability of each layer to serve as both an input and an output layer allows the ANN to understand increasingly complex things. 

The units in the neural layer make an effort to learn about the information by weighing the gathered data in line with the underlying logic of the ANN. Units are able to provide transformed results using these rules, which are then output to the subsequent layer. 

Types and Uses of Artificial Neural Networks in Machine Learning: 

The following are the types and uses of Artificial Neural Networks in Machine Learning: 

Feedforward Neural Network (Artificial Neuron):  

The fact that all the information only goes in one way makes this neural network the most fundamental artificial neural network type used in machine learning. This kind of neural network’s output nodes, which may include hidden layers, are where data exits and enters. The categorizing activation function is used in this neural network. Backpropagation is not permitted; only front-propagated waves are allowed. 

Feedforward neural networks have many uses, including speech recognition and computer vision. These kinds of neural networks are simpler to maintain and respond very well to noisy data. 

Radial Basis Function Neural Network:  

Basic radial functions take into account a point’s separation from the center. RBF functions contain two layers: the inner layer, where the Radial Basis Function and features are merged, and the outer layer, where the output of these features is taken into account when computing the same output in the subsequent time step, which is essentially a memory. Applications of this neural network exist in power restoration systems.  

Kohonen Self-Organizing Neural Network: 

Kohonen networks, also known as knets or self-organizing maps, are a type of neural network that performs clustering. When you don’t initially know the groups in the dataset, you can use this kind of network to cluster the data into clear groups. 

Recurrent Neural Network (RNN) – Long Short-Term Memory:  

To enhance layer prediction, a recurrent neural network saves a layer’s output and feeds it into the input. When the output of the first layer is computed, the recurrent neural network starts. The first layer of the RNN is pretty comparable to the feed-forward neural network. Each unit will then continue to hold onto some data from the layer above, enabling it to function as a computational memory cell. 

Convolutional Neural Network:  

Since their inception, convolutional neural networks have been almost exclusively used for computer vision applications. Their architecture is made especially for carrying out intricate visual assessments. Instead of the usual 2-D array, the convolutional neural network architecture is characterized by a 3-Dl layout of neurons. In these neural networks, the convolutional layer is the first layer. Only a small percentage of the visual field is processed by each neuron in the convolutional layer. After the convolutional layers, corrected layer units, or ReLU, are applied, allowing the CNN to handle complex input. 

Modular Neural Network:  

A modular neural network in artificial intelligence is made up of various unique neural networks that act independently of one another to produce the desired output. The several neural networks each carry out a particular sub-task by receiving unique inputs from other networks. This modular neural network can reduce complexity by breaking a complex and time-consuming computation into simpler steps while still producing the desired result. 

Conclusion: 

Artificial Neural Networks in Machine Learning are developed to simulate the human brain digitally. Because Artificial Neural Networks are used for complex analysis in a variety of disciplines ranging from engineering to medical science, these networks can be employed to develop the next generation of computers. In this way, human cognitive cues may be converted into signals that machines can understand. In the future, our interactions with the environment may be cognitive. For more information and professional certification in this domain, check out UNext’s Executive PG Diploma in Management & Artificial Intelligence.