4 Types of Neural Network and Their Applications In Detail

A complete study of Neural Networks

Before we begin, it's important to understand what a neural network is. We're attempting to make this as simple as possible. So, Neural Networks are a set of algorithms that imitate the processes of a human brain in order to identify connections between large volumes of data. 

It's also utilized in financial services applications like fraud detection and risk assessment. The term "neural network" refers to a system of neurons that might be biological or man-made. A neural network is comparable to the neural network in the human mind. 

In a neural network, a "neuron" is a numerical measure that gathers and categorizes data using a specified design. Warren McCulloch, a neurophysiologist, and Walter Pitts, a mathematician, created the first artificial neural network in 1943 as a consequence of their study into using mathematics, specifically Boolean logic, to describe how neurons in the brain function.

The most significant benefit of neural networks is their ability to learn on their own and generate output that is not constrained by the input. Data loss has no influence on the system's functionality because the data is maintained in its own networks rather than a database. Later, we'll look at the benefits of neural networks in full depth.

Types of Neural Networks:

There are four types of neural networks which are popularly known as Perceptron, Feedforward network, Multi-layer perceptron, and RBNs 

  1. Perceptron: A single-layer neural network is referred to as a perceptron, but a multi-layer perceptron is referred to as a neural network. The perceptron is the simplest and oldest type of neural network. It is made up of just one neuron that takes input and applies an activation function to it in order to generate a binary output. There are no hidden layers in this algorithm, and it can only be used to tackle binary classification problems. The neuron handles the addition of input values and associated weights. After that, the total is sent to the activation function, which outputs a binary value.
  2. Feedforward network: Feed Forward (FF) networks are made up of many neurons and hidden layers that are all linked. These are referred to as "feed-forward" because data solely flows forward and there is no backward propagation. Depending on the application, hidden layers may or may not be present in the network. The more layers there are, the more weights may be customized. As a result, the network's capacity to learn may improve.
  3. Multi-layer Perceptron: The inability of Feed-Forward networks to learn with backpropagation was their primary flaw. Neural networks with several hidden layers and activation functions are known as multi-layer perceptrons. The weights are updated using Gradient Descent in a Supervised way. The forward propagation of the inputs and the backward propagation of the weight updates are bi-directional in a multi-layer perceptron.
  4. Radial Basis Networks: RBNs (radial basis networks) anticipate targets in a fundamentally new way. It has three layers: an input layer, an RBF neural layer, and an output layer. For each of the training data examples, the RBF neurons record the actual classes. Because the RBN uses a Radial Function as an activation function, it differs from a traditional Multilayer Perceptron. The RBF neurons check the Euclidean distance of the feature values with the actual classes recorded in the neurons when fresh data is input into the neural network. This is comparable to determining which cluster a certain instance belongs to. The projected class is allocated to the class with the shortest distance.

What problem can Neural Network solve?

Neural networks are used to handle a variety of commercial problems, including sales forecasting, consumer research, data validation, and risk management. At Statsbot, for example, we employ neural networks to forecast time series, discover data abnormalities, and parse natural language. To mention a few applications, neural networks can aid in classification, prediction, filtering, optimization, pattern recognition, and function approximation. Their capacity to decipher complicated, noisy, or nonlinear data is one of their greatest assets. Despite the fact that neural networks can solve the bulk of machine learning issues, they are not always the best option. A neural network can solve problems that a human can solve with tiny amounts of data.

Application of Neural Networks

Speech Recognition: 

Speech recognition is the skill of a computer or program to recognize words spoken aloud and transform them into readable text. It adapts to human speech's extremely varied and context-dependent character. Computer science, linguistics, and computer engineering research are all used in speech recognition. Speech recognition features are included in many current gadgets and text-focused apps to make using a device easier or hands-free.

Although significant progress has been achieved in this sector, such systems still face issues such as restricted vocabulary or grammar, as well as the need for the system to be retrained for various speakers in different situations. In this regard, ANN plays a crucial role. For voice recognition, the following ANNs were used:

  • Multilayer networks
  • Multilayer networks with recurrent connections
  • Kohonen self-organizing feature map

Human Face recognition: 

The face is recognized using neural networks that learn the proper categorization of the coefficients generated by the eigenface method. The network is trained on photos from the face database before being used to recognize the faces that are shown to it. In a database of 40 face pictures, four individuals (people) were utilized. With vertically oriented frontal images of a human face, an identification accuracy of 95.6 percent was attained.u

Character Recognition: 

Individual characters in graphics are recognized using neural networks. One of the items utilized to adapt the application to the client's expectations is the confidence of each recognition, which is supplied by the neural network as part of the classification result. It's an intriguing issue that falls under the umbrella of Pattern Recognition in general. For the automated recognition of handwritten characters, whether letters or numbers, many neural networks have been created. Multilayer neural networks, such as Backpropagation neural networks, have been utilized for character recognition and, Neocognitron

Companies using Neural Networks

Neural networks can be used to solve a wide range of real-world issues. They've already been shown to be effective in a variety of sectors. Neural networks are ideally suited for prediction or forecasting purposes such as sales forecasting because they excel at recognizing patterns or trends in data. "Neural networks are increasingly being used to solve various business problems," said Nir Bar-Lev, co-founder and CEO of deep learning platform Allegro.ai. "Thanks to their ability to quickly categorize and recognize reams of information, virtually every tech titan — including Google, Microsoft, and Amazon — is investing more in neural networks to solve various business problems." Following are the Companies that specialize in neural networks:-

Deeplite: from cloud to edge computing, providing an AI-driven optimizer to make deep neural networks quicker, smaller, and more energy-efficient. Companies that specialize in neural networks.

Peltarion: Peltarion is a software engineering firm dedicated to bringing Artificial Intelligence accessible and cheap to any business on the planet. Peltarion intends to use neural networks to solve real-world issues in a number of disciplines.

Speechmatics: Speechmatics transforms voice to text, allowing users to search and analyze audio files. Speechmatics offers the most accurate and cost-effective speech recognition available anywhere in the globe.

How is a neural network used in our daily life?

In most situations, neural networks are used to solve problems in business management. The use of neural networks in real-world commercial applications is exploding. For organizations that utilize analytics, marketing, or fraud detection, NNs have already become the technique of choice in some situations. Artificial intelligence (AI) and neural networks offer enormous potential for assisting human decision-making in a variety of fields. Although the area of neural networks and its use of big data is difficult and high-tech, it is the most effective approach to improve efficiency and its ultimate goal is to serve people.

Advantage of Neural Networks

  1. The output generated by the data might be partial or insufficient after the ANN has been trained. The absence of performance is determined by the relevance of the missing information.
  2. The output generation of an artificial neural network is unaffected by the corruption of one or more cells. This improves the networks' ability to tolerate failures.
  3. The network can identify a problem even if a neuron is not responding or a piece of information is missing and still generate the output.
  4. These networks have high numerical strength, allowing them to execute several functions at once.
  5. Similarly to how information is kept on a network rather than a database in traditional programming. When a few pieces of data vanish from one location, the network as a whole continues to operate.
  6. It is important to outline the instances and educate the network according to the intended output by displaying those examples to the network for an artificial neural network to learn. The number of instances chosen determines how far the network progresses.

Disadvantages of Neural Networks

  1. The structure of artificial neural networks is determined by no precise rule. Experience and trial and error are used to create the ideal network structure.
  2. The construction of Artificial Neural Networks necessitates parallel processing power. As a result, the equipment's implementation is contingent.
  3. This is the most serious issue with ANN. When ANN provides a probing solution, it does not explain why or how. The network's trust is eroded as a result.+
  4. Numerical data can be used by ANNs. Before introducing ANN to a problem, it must first be converted into numerical numbers. The network's performance will be influenced directly by the display method that is used. This is contingent on the user's capabilities.
  5. When the network's error on the sample is lowered to a specific amount, the training is complete. The value does not produce the best outcomes.
  6. The "black box" character of the model, the higher computing cost, the risk of overfitting, and the empirical nature of model creation are all disadvantages.

What does the future hold?

It's difficult to predict if neural net research will continue eternally or whether new, more advanced technologies will take their place, but in either case, this AI accomplishment demands your attention. As the scale of neural networks grows at a rapid rate, the technology's ability to handle increasingly difficult issues becomes more realistic. The combination of bigger neural networks, greater processing power, larger datasets, and the findings of decades of study provides a promising future for the use of Artificial Neural Networks to help society, as long as AI ethics are integrated. Researchers will be able to comprehend the relevance and need of neural networks' involvement in the creation of a human-like artificial brain if they have a better knowledge of its future and applications. Artificial Intelligence research aims to build artificial life, which would be impossible to produce without a brain-like processing unit that is an intrinsic component of its behavioral and physical structure.


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