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Introduction to Artificial Neural Network
As we all know, the most important part of our body is our brain which allows us to think, react, and perform major tasks in our day-to-day life. Neuron being the basic unit of the brain or the nervous system enables the brain to receive sensory inputs and then send the output to other parts of the body. Each and every Neuron is connected to another neuron through their synapses. The network formed by these neurons in our body can be termed a Neural network.
Now, you might wonder what is the relation between the neural network present inside our body and the Artificial neural network. Let’s deep dive into our topic and learn more about Artificial neural networks and its application.
Artificial neural network
Artificial Neural Networks are computational models inspired by the human brain. Similar to a human brain has neurons interconnected to each other, artificial neural networks also have neurons which are called nodes that are linked to each other in various layers of the networks. Each connection, like the synapses in the biological neuron, can receive and send the signals to other neurons.
Basically, an Artificial Neural Network consists of an input layer, a hidden layer, and an output layer. The input layer receives data from the outside environment. Once the data is received, it is analyzed and learned by the model. Then this data passes through one or multiple hidden layers that convert the data into essential information so that the output can use it. At last, the output layer delivers an output in the form of an artificial neural network’s response to input data.
Artificial neural network vs biological neural network
Structurally, an Artificial neuron is similar to that of a biological neuron. A biological neuron has an axon that transmits the impulses to other neurons, dendrites that receive them, and a cell body, that processes the impulses. Similarly, Artificial neural networks have input nodes that receive input signals, compute these input signals in the hidden layer, and then process the results in the output layer using activation functions to compute the final output.
| Characteristics | Artificial Neural Network | Biological (Real) Neural Network |
|---|---|---|
| Speed | Faster in processing information. Response time is in nanoseconds. | Slower in processing information. The response time is in milliseconds. |
| Processing | Serial processing. | Massively parallel processing. |
| Size & Complexity | Less size & complexity. It does not Perform complex pattern recognition tasks. | A highly complex and dense network of interconnected neurons containing neurons of the order of 1011 with 1015 of interconnections.<strong |
| Storage | Information storage is replaceable means replacing new data with an old one. | Less size & complexity. It does not Perform complex pattern recognition tasks. |
| Fault tolerance | Less size & complexity. It does not Perform complex pattern recognition tasks. | Information storage is adaptable means new information is added by adjusting the interconnection strengths without destroying old information. |
| Control Mechanism | Fault intolerant. Corrupt information cannot be retrieved in case of failure of the system. | No specific control mechanism external to the computing task. |
Basic terms used in ANN
Weights: A collection of array values that are multiplied by the corresponding input values. The weights are essentially reflecting how important an input is.
Bias: Bias is simply a constant value (or a constant vector) that is added to the product of inputs and weights. Bias is utilized to offset the result. The bias is used to shift the result of the activation function towards the positive or negative side.
Input Layer: As the name suggests, it accepts inputs in several different formats provided by the programmer. Hidden Layer: The hidden layer presents in between the input and output layers. It performs all the calculations to find hidden features and patterns.
Output Layer: The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer.
Activation Function: The primary role of the Activation Function is to transform the summed weighted input from the node into an output value to be fed to the next hidden layer or as output. the purpose of an activation function is to add non-linearity to the neural network.
Working of artificial neural network
In the first step, Input units are passed i.e. data is passed with some weights attached to it to the hidden layer. We can have any number of hidden layers as per our needs. Each hidden layer consists of neurons and all the inputs are connected to each neuron. After passing on the inputs, all the computation is performed in the hidden layer.
In The hidden layer, all the inputs are multiplied by their weights. Weights are the gradient or coefficient of each variable. It shows the strength of the particular input. After assigning the weights, a bias variable is added. Bias is a constant that helps the model to fit in the best way possible. Then, the activation function is applied to the equation. The activation function is a nonlinear transformation that is applied to the input before sending it to the next layer of neurons. The importance of the activation function is to inculcate nonlinearity in the model.
After passing through every hidden layer, we move to the last layer i.e. our output layer which gives us the final output. After getting the predictions from the output layer, the error is calculated i.e. the difference between the actual and the predicted output. If the error is large, then the steps are taken to minimize the error and for the same purpose, Back Propagation is performed.
Types of Artificial Neural Networks
There are several kinds of artificial neural networks. These types of networks are implemented based on mathematical operations and a set of parameters required to determine the output. Let’s look at some of the neural networks:
Feedforward Neural Network:-
The feedforward neural network is one of the simplest forms of ANN, where the data or the input travels in one direction. The data passes through the input nodes and exits on the output nodes. This neural network may or may not have hidden layers. In simple words, it has a front-propagated wave and no backpropagation by using a classifying activation function.
2. Convolutional Neural Network:
CNN is similar to that of the feed-forward neural network in which each node is assigned a weight. CNNs are designed to analyze data that looks like a grid, including photos and movies. They excel at tasks like picture classification, object identification, and image synthesis because they use convolutional layers to automatically recognize hierarchical patterns and features inside data. CNNs are renowned for their proficiency in handling big, high-dimensional datasets, and have contributed significantly to the advancement of computer vision applications.
3. Recurrent or Feedback Network
RNN or feedback neural network is the second kind of ANN model, in which the outputs from neurons are used as feedback to the neurons of the previous layer. In other words, the current output is considered as an input for the next output. RNN (Fig. 4) is mainly used for dynamic information processing like time series prediction, processing control, and so on. Hopfield network and perceptron with feedback are the popular types of this network.
4. Radial Basis Function Networks
RBFNs use radial basis functions as activation functions in their hidden layers. They are frequently used in regression and function approximation applications. When it comes to issues that call for localized solutions, including interpolation and approximation of non-linear functions, RBFNs are helpful. They have been used in disciplines including engineering for system modeling and control and finance for option pricing.
Application of Artificial Neural Network
Image Recognition: Artificial neural networks (ANNs) are widely used in image recognition tasks, such as facial recognition They have applications in security, autonomous cars, and medical image analysis since they can analyze pixel data and learn to identify patterns and characteristics in images.
Healthcare Diagnosis: In order to find patterns and generate precise predictions, ANNs may analyze complicated medical data, including patient records, pictures, and genetic information. For instance, they can spot early indications of conditions like diabetes, Alzheimer’s, or cancer, allowing for prompt intervention and treatment planning.
Financial Forecasting: ANNs are widely used in the financial sector for activities like risk assessment, fraud detection, and stock market forecasting. To forecast asset prices and market behavior, they examine previous financial data, news mood, and market patterns. These networks can spot trends that are hard for conventional models to pick out, assisting with wise investment choices and lowering risk exposure.
Autonomous Vehicles and Robotics: ANNs play a crucial role in autonomous vehicles and robotics. To make quick decisions for secure navigation and control, they analyze sensor data from cameras, radar, and LiDAR. Deep neural networks are used by self-driving cars for course planning, object identification, and lane tracking. Robots can do tasks ranging from straightforward pick-and-place operations to intricate problem-solving thanks to ANNs in robotics, which let them learn about and adapt to their surroundings.
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