Hello! It is going to be both interesting and important. Ever written a code and then entered different datasets in the input? You must have realized that whatever you have trained the code to do to one dataset, it does the same with all the datasets. This is how a computer learns anything.
But what if a computer starts to learn like a human mind learns? Sounds fascinating doesn’t it? Yes it is. There is a computational algorithm that has interconnected nodes.
These nodes act like neurons. Yes, the same neurons that are present in a human mind. So here, a computer learns like a human mind does. They can discover hidden patterns and correlations in raw data using algorithms, cluster and categorise it, and learn and improve over time.
This type of computational systems are called neural networks.
In this blog, we are going to discuss the types of neural networks. But first of all keeping the rituals alive, let us know about what neural networks are.
What are Neural Networks?
Neural networks are a subset of machine learning that are at the heart of deep learning algorithms. They are also known as artificial neural networks or simulated neural networks. Their name and structure are derived from the human brain, and they resemble the way biological neurons communicate with one another.
A node layer contains an input layer, one or more hidden layers, and an output layer in artificial neural networks (ANNs). Each node, or artificial neuron, is connected to the others and has a weight and threshold linked with it. If a node's output exceeds a certain threshold value, the node is activated, and data is sent to the next tier of the network. Otherwise, no data is sent on to the network's next tier.
Now let us move ahead and look at the types of Neural networks.
Types of Neural Networks
There are many different types of neural networks that are now available or under development. They can be categorised based on their: Structure, data flow, neuron density, layers, and depth activation filters, to name a few.
Here are the main types of neural networks:
The most basic and oldest type of neural network is the perceptron. It is made up of only one neuron that accepts the input and applies an activation function to it in order to generate a binary output. There are no hidden layers in this model, and it can only be used for binary classification problems.
The addition of input values with their weights is processed by the neuron. After that, the generated sum is transferred to the activation function, which generates a binary output.
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Feed Forward Network
Feed Forward (FF) networks are made up of numerous neurons and hidden layers that are all coupled. 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 levels there are, the more weights can be customized. As a result, the network's ability to learn will improve. Because there is no backpropagation, the weights are not changed. The activation function receives the output of the weight multiplication with the inputs, which functions as a threshold value.
FF networks are used in the following applications:
Radial basis function Neural Network
Radial Basis Networks (RBN) anticipate targets in a fundamentally different way. It is made up of three layers: an input layer, a layer with RBF neurons, and an output layer. The actual classes for each of the training data examples are stored in the RBF neurons. Because the Radial Function is utilised as an activation function, the RBN differs from a traditional Multilayer Perceptron.
The RBF neurons check the Euclidean distance of the feature values with the actual classes stored in the neurons when new data is introduced into the neural network. This is comparable to determining which cluster a specific instance belongs to. The projected class is assigned to the class with the shortest distance.
These are mostly used in power restoration systems.
Multi layer perceptron
The Feed Forward networks' fundamental flaw was their inability to learn using backpropagation. Perceptrons with numerous hidden layers and activation functions are known as multi-layer perceptrons. The learning is done in a Supervised mode, with the weights being changed using Gradient Descent.
The Multi-layer Perceptron is bi-directional, with inputs propagating forward and weight changes propagating backward. Depending on the type of target, the activation functions can be altered. Softmax is commonly used for multi-class classification, while Sigmoid is commonly used for binary classification. Because all of the neurons in one layer are connected to all of the neurons in the next layer, these are also known as dense networks.
Convolution Neural Networks
Instead of a two-dimensional array, a convolution neural network has a three-dimensional layout of neurons. A convolutional layer is the first layer. Each convolutional layer neuron only analyses data from a limited portion of the visual field. Like a filter, input features are taken in batches. The network decodes images in chunks and can perform these operations numerous times to complete the entire image processing. The image is converted from RGB or HSI to grayscale during processing. Further variations in pixel value will aid in the detection of edges, allowing images to be categorised into several categories.
A convolution neural network has a three-dimensional architecture of neurons rather than a two-dimensional array. The first layer is a convolutional layer. Each convolutional layer neuron examines only a small section of the visual field. Input features are collected in batches, much like a filter. The network decodes images in chunks and can repeat these processes multiple times in order to complete the image processing. During processing, the image is transformed from RGB or HSI to grayscale. More pixel value fluctuations will aid in the detection of edges, allowing images to be classified into many categories.
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Recurrent Neural Networks
The Recurrent Neural Network is based on the notion of preserving a layer's output and feeding it back into the input to help forecast the layer's outcome.
The first layer is built in the same way as a feed forward neural network, with the sum of the weights and features as the product. Once this is computed, the recurrent neural network process begins, which means that each neuron will remember some information from the previous time step from one time step to the next.
As a result, each neuron performs computations as if it were a memory cell. We must allow the neural network to operate on front propagation and remember what information it requires for later usage in this process. If the prediction is incorrect, we use the learning rate or error correction to make minor changes so that the back propagation will progressively work towards making the correct prediction.
LSTM (Long Short-Term Memory) Networks
LSTM networks are a sort of RNN that employs a combination of special and standard units. A Memory cell is included in LSTM units, which can store data for lengthy periods of time.
When information enters the memory, when it is output, and when it is forgotten, a system of gates is used to govern it. Input gates, output gates, and forget gates are the three types of gates.
The input gate determines how much data from the previous sample will be stored in memory; the output gate controls the quantity of data sent to the next layer; and forget gates govern the memory tearing rate. They can learn longer-term dependencies thanks to this architecture.
Modular Neural Network
Modular Neural Networks are made up of a number of separate networks that each act independently and contribute to the final result. In comparison to other networks creating and performing sub-tasks, each neural network has its own set of inputs. In order to complete the tasks, these networks do not interact or communicate with one another.
A modular neural network has the advantage of breaking down a huge computational process into smaller components, reducing complexity. This decomposition reduces the amount of connections and eliminates the interaction of these networks with one another, resulting in faster processing. The processing time, on the other hand, will be determined by the number of neurons involved in the computation of the findings.
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If you keep adding layers to a neural network, it can quickly become incredibly complex. There are occasions when we can take advantage of the extensive research in this field by employing pre-trained networks. This is called transfer learning.
In this blog, we tried to cover all the main types of neural networks. Hope it helps you the next time you use any software to implement neural networks. Till then, goodluck.