What Are Neural Networks, Its Types?

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Neural Networks

Artificial neural networks (ANNs) and simulated neural networks (SNNs) are a subset of machine learning that are at the heart of deep learning methods. 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 linked to the others and has its own weight and threshold. 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.
Training data is used by neural networks to learn and increase their accuracy over time. In computer science and artificial intelligence, these learning techniques can be used to quickly identify and cluster data. 
When compared to manual identification by human experts, tasks in speech recognition or image recognition can take minutes rather than hours. Google's search algorithm is one of the ...
... most well-known neural networks.

Types of neural networks

Neural Networks are a subset of Machine Learning techniques that use Neurons and Hidden Layers to learn data and patterns in a unique way. Because of their intricate structure, Neural Networks are far more powerful than typical Machine Learning algorithms and can be employed in areas where traditional Machine Learning methods are insufficient. 
Distinct types of neural networks exist, each of which is employed for a different purpose. While this isn't an exhaustive list, the following are some of the most popular types of neural networks that you'll come across for common applications:

1. Perceptron
2. Feed Forward Networks
3. Multi-Layer Perceptron
4. Radial Based Networks
5. Convolutional Neural Networks
6. Recurrent Neural Networks
7. Long Short-Term Memory Networks

Perceptron

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.

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 back propagation, the weights are not changed. The activation function receives the output of the weight multiplication with the inputs, which functions as a threshold value.
These networks are used in:

- Classification
- Speech recognition
- Face recognition
- Pattern recognition

Multi-Layer Perceptron

The Feed Forward networks' fundamental flaw was their inability to learn using back propagation. 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.
They are employed in Deep Learning applications, however because of their intricate structure, they are often slow.

Radial Basis Networks

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 utilized 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.
RBNs are mostly employed in function approximation applications, such as Power Restoration systems.

Convolutional Neural Networks

Convolutional neural networks are the most commonly employed in picture classification (CNN). CNNs have numerous convolution layers that extract essential characteristics from images. It is responsible for low-level details and higher-level features.
Convolution maps the input image using a custom matrix, often known as filters. These filters are updated using back propagation. The Canny Edge Detector, for example, finds the edges in any image.
The pooling layer follows the convolution layer and is responsible for aggregating the maps produced by the convolution layer. It can be Max, Min, etc. CNNs contain dropout layers that make individual neurons inert to reduce over fitting and speed up convergence.
In the hidden layers, CNNs use Rectified Linear Units (ReLU). Activation functions for classification and regression are Softmax and ReLU respectively.

Recurrent Neural Networks

Recurrent Neural Networks are used to predict using sequential data. Sequential data includes images, words, etc. Unlike a feed-forward network, the RNN layers receive a time-delayed input of the prior occurrence prediction. This prediction is kept in the RNN cell, which is a second input.
RNN has the Vanishing Gradient problem, which makes it difficult to remember previous layers' weights.

Long Short-Term Memory Networks

This is achieved by adding a unique memory cell that can store information for long periods of time. LSTM employs gates to decide which outputs to use. It has 3 gates: Input, Output, and Forget. What data should be stored is determined by the input gate. The output gate regulates data sent to the next layer, whereas the forget gate controls data dumping/forgetting.
LSTMs are used in various applications such as:

- Gesture recognition
- Speech recognition
- Text prediction

Conclusion

Neural Networks can get very complex within no time you keep on adding layers in the network. There are times where we can leverage the immense research in this field by using pre-trained networks for our use.
This is called Transfer Learning. In this tutorial, we covered most of the basic neural networks and their functioning. Make sure to try these out using the Deep Learning frameworks like Keras and Tensorflow.
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