All You need to Know about Artificial Neural Networks (ANN)

The human brain is integrated into a machine using deep learning, a subset of machine learning. It is a set of neural network algorithms that attempts to mimic the functioning of a human brain and learn from experiences since it is inspired by how a human brain works.

 

Artificial neural networks are a branch of artificial intelligence inspired by biology and fashioned after the brain. A computational network based on biological neural networks, which create the structure of the human brain, is typically referred to as an artificial neural network. Artificial neural networks also feature neurons that are linked to each other in different layers of the networks, just like neurons in a real brain. Nodes are the name for these neurons.

 

What are Artificial Neural Networks (ANN)?

 

The biological neural networks that shape the structure of the human brain are where the phrase "artificial neural network" originates. Artificial neural networks also feature neurons that are interconnected to one another in different levels of the networks, just like the human brain, which has neurons that are connected to one another. Nodes are the name for these neurons.

 

There are three or more interconnected layers in an artificial neural network. Neurons in the input layer make up the first layer. These neurons transmit information to the deeper layers, which then transmit the final output information to the output layer at the end.

 

The units that make up the inner layers, which are all hidden, adaptively transform the information that is passed from layer to layer. Each layer functions as both an input and an output layer, enabling the ANN to comprehend more intricate objects. The neural layer is the collective name for these inner layers.

 

By weighing the collected data in accordance with the ANN's internal logic, the units in the neural layer attempt to learn about the information. These rules enable units to produce a transformed outcome, which is subsequently sent as an output to the following layer.

 

How do Artificial neural networks work?

 

To make sense of the data it is fed, artificial neural networks use a variety of mathematical processing levels. An artificial neural network typically consists of hundreds to millions of units, also known as artificial neurons, organized in a hierarchy of layers. Various types of information are sent to the input layer from the outside world. The network wants to process or learn about this data. The data is sent through one or more hidden units after leaving the input unit. It is the hidden unit's responsibility to change the input such that the output unit can use it.

 

Most neural networks have complete layer-to-layer connectivity. Similar to a human brain, these connections are weighted; the bigger the number, the greater influence one unit has on another. The network is learning more about the data as it passes through each unit. The output units are located on the opposite side of the network, and it is here that the network reacts to the data that it has received and processed.

 

Since the original artificial neural network was created by computer scientists, cognitive neuroscientists have learned a great deal about the human brain. One of the things they discovered was that the brain is organized hierarchically and those different areas of the brain are in charge of processing various aspects of information. Input enters the brain, each level of neurons offers to understand, and then the information is transferred to the following, more senior level. ANNs are attempting to mimic this mechanism accurately.

 

An enormous amount of data, known as a training set, must be presented to ANNs in order for them to learn.

 

Also Read | Basics of Modular Neural Networks

 

The architecture of Artificial Neural Networks:

 

Artificial neural networks act in a manner akin to how neurons in the human nervous system function. The term "Neural Networks" was first used by Warren S. McCulloch and Walter Pitts in the early 1970s. Let's first examine the construction of ANNs in order to comprehend how they function. Three layers are required in a neural network:

 

• Input Layers: The input layer, which is the top layer of an ANN, is where input data in the form of letters, numbers, audio files, image pixels, etc. is first received.

 

• Hidden Layers: The hidden layers are located in the center of the ANN model. One hidden layer, like in the case of a perceptron, or several hidden layers are both possible. On the input data, these hidden layers run a variety of mathematical operations and identify the patterns that are there.

 

• Output Layer: The middle layer's meticulous computations enable us to produce the desired result in the output layer. Numerous factors and hyperparameters of a neural network influence how well the model performs. The majority of these characteristics determine how an ANN performs. Weights, biases, learning rates, batch sizes, and other parameters are a few of these. In an ANN, each node carries some weight.

 

Weights are assigned to each node in the network. Calculating the weighted total of the inputs and the bias involves using a transfer function.

 

The activation function gets the outcome after the transfer function computes the sum. The activation functions fire the appropriate result from the node based on the output they have received.

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Different Layers of Artificial Neural network

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Training Algorithms for Artificial neural network: 

 

• Gradient Descent Algorithm: 

 

In the case of a supervised training model, this is the most basic training algorithm employed. If the actual output deviates from the desired output, the error or difference is identified. The network's weights are adjusted via the gradient descent technique to reduce this error.

 

• Back Propagation Algorithm: 

 

It is a development of the delta learning rule based on gradients. In this case, the mistake is propagated backward from the output layer to the input layer via the hidden layer after being discovered (the difference between intended and target). In the case of multi-layer neural networks, it is employed.

 

Also Read | Introduction to Residual Network (ResNet)

 

Types of Artificial Neural Networks:

 

1. Feedforward Neural Network: 

 

One of the most fundamental artificial neural networks is the feedforward neural network. The data or input used in this ANN only moves in one direction. While hidden layers may or may not exist, it enters the ANN through the input layer and exits through the output layer. 

 

As a result, the feedforward neural network often only has front propagation and no backpropagation.

 

2. Recurrent Neural Networks: 

 

The Recurrent Neural Network stores a layer's output and feeds it back into the input to improve layer prediction. The first layer of the RNN is relatively similar to the feed-forward neural network, and once the output of the first layer is computed, the recurrent neural network begins. 

 

Each unit will retain some information from the layer above after this, enabling it to function as a memory cell for calculations.

 

3. Convolutional Neural Networks: 

 

The feed-forward neural network, in which the connections between units contain weights that indicate the influence of one unit on another unit, and the convolutional neural network share certain characteristics. 

 

However, a CNN has one or more convolutional layers that perform a convolution operation on the input before passing the final output to the following layer. CNN includes voice and image processing applications that are especially helpful in computer vision.

 

4. Modular Neural Network:

 

A modular neural network is made up of a variety of discrete neural networks that each work separately to produce the desired output without communicating with one another. By acquiring distinct inputs from other networks, each of the several neural networks executes a different sub-task. 

 

The benefit of this modular neural network is that it can reduce the complexity of a vast and complex computational process while still producing the desired output by breaking it up into smaller components.

 

5. Radial basis function Neural Network: 

 

The distance between a point and its center is taken into account by radial basis functions. Functions with RBF have two layers. The input is translated into each of the hidden layer's radial basis functions in the first layer, and the output layer then computes the result in the following step. In order to model the data that represents any underlying trend or function, radial basis function nets are typically used.

 

Applications of Artificial Neural Networks:

 

Here are some significant ANN applications:

 

• Speech recognition: 

 

Artificial neural networks are frequently utilized in speech recognition. A statistical model known as the Hidden Markov Model was employed in earlier voice recognition methods. Several types of neural networks are now the sole technique to learn precise classification thanks to the advent of deep learning.

 

• Recognition of Handwritten Characters: 

 

ANNs are used to identify handwritten characters. Neural networks have been trained to recognize handwritten characters, which can be letters or numbers. 

 

• Signature Classification: 

 

When creating these authentication systems, we use artificial neural networks to recognize signatures and classify them according to the person signing them. Additionally, neural networks can determine the veracity of a signature.

 

• Medical: 

 

It can be applied to MRI image analysis and cancer cell detection to produce comprehensive results.

 

Also Read | Advantages and Disadvantages of Neural Networks

 

Advantages of Artificial Neural Networks(ANN):

 

1. Processing in parallel capability: Artificial neural networks have a numerical value that allows them to carry out multiple tasks at once.

 

2. Storing data on the entire network: Traditional programming does not use a database; instead, it stores data on the entire network. The network continues to function even if some data disappears from one location temporarily.

 

3. Capability to work with incomplete knowledge: Following ANN training, the data may still produce output even with insufficient data. The significance of the missing data in this situation is what causes the performance loss.

 

4. Having a distribution of Memory: Determining the examples and motivating the network in accordance with the desired output by showing it these examples is crucial for ANN to be able to adapt. The network's output can be false if the event can't be represented by the network in all of its aspects because the network's succession is directly proportional to the selected instances.

 

5. Having Fault Tolerance: The network is fault-tolerant because the expropriation of one or more ANN cells does not prevent the network from producing output.

 

Also Read | Convolutional Neural Network (CNN)

 

Disadvantages of Artificial Neural Networks:

 

1. Guarantee of appropriate network architecture: The structure of artificial neural networks is not determined by any specific rules. Through experience, trial, and error, the right network structure is achieved.

 

2. Unrecognized network behavior: It is the most important ANN issue. When an ANN generates a testing solution, it doesn't explain why or how. It erodes network confidence.

 

3. Hardware reliance: According to their structure, artificial neural networks require processors with parallel processing power. As a result, the equipment's realization is dependent.

 

4. Having trouble getting the network to see the problem: ANNs can process data that is numerical. Before using ANN, problems must be transformed into numerical values. The network's performance will be directly impacted by the presentation mechanism that must be decided here. It is dependent on the user's skills.

 

5. Unknown is the network's lifespan: The network is reduced to a particular error value, and this error value does not produce the best outcomes for us.

 

Also Read | Top AI Technologies Used in 2022

 

Conclusion:

 

The ANN is a very helpful model that can be used in machine learning and problem-solving. The world of computing stands to benefit greatly from neural networks. They are therefore very adaptable and powerful due to their capacity to learn through example. Therefore, it is crucial to comprehend the potential as well as the constraints of the Neural Network in order to make the best use of it for various problems.