Types of Neural Networks used by Artificial Intelligence
Neural Networks used by Artificial Intelligence
Neural Networks are a subset of Machine Learning techniques which learn the data and patterns in a different way. It also utilizes Neurons and Hidden layers. Neural Networks are powerful due to their complex structure and can be used in applications by the students of Best BTech College in Jaipur.
What are Neural Networks?
Neural Networks use the architecture of human neurons with multiple inputs, a processing unit, and single/multiple outputs. There are weights related with each connection of neurons. By adjusting these weights, a neural network arrives at an equation which is used for predicting outputs on new unseen data by the students of engineering colleges in Jaipur. It can be done by backpropagation and updating of the weights.
Various Types of Neural Networks
Different types of neural networks are used by the students of BTech colleges for different data and applications. The different architectures of neural networks are particularly designed to work on those types of data or domain. There are some basic and some complex ones includes the following:
The Perceptron is the most basic form of neural networks. It consists of just 1 neuron which takes the input and applies activation function to produce a binary output. It does not contain any hidden layers and can be further used for binary classification tasks. Additionally, the neuron does the processing of input values with their weights. Then, it passes to the activation function to produce a binary output.
Feed Forward Network
The Feed Forward (FF) networks consist of various neurons and hidden layers which are connected to each other. These are called “feed-forward”, as the data flows in the forward direction only, and there is no backward propagation. Hidden layers might not be necessarily present in the network as per the application.
With a greater number of layers, weights can be customized. Hence, it includes the ability to learn the network. The output of multiplication of weights with the inputs is fed to the activation function that mainly act as a threshold value.
FF networks are used in Classification, Speech recognition, Face recognition, Pattern recognition, Multi-Layer Perceptron, etc by the students of BTech colleges in Jaipur. The important shortcoming of the Feed Forward networks was its inability to learn with backpropagation. Multi-layer Perceptrons are the neural networks which incorporate all hidden layers and activation functions. The learning takes place in a Supervised manner where the weights are updated through the Gradient Descent.
Radial Basis Networks
Radial Basis Networks (RBN) use different methods to predict the targets. It consists of an input layer, a layer with RBF neurons along with an output. The RBF neurons store the actual classes for all training data instances. The RBN are different from the usual Multilayer perceptron because of the Radial Function used as an activation function by the students of top engineering colleges in Jaipur.
When the new data is fed into the neural network, the RBF neurons compare the feature values with the actual classes stored in the neurons. This is similar to finding which cluster to does the specific instance belong. The class where the distance is minimum is assigned like the predicted class. The RBNs are used mostly in function approximation applications such as Power Restoration systems.
When it comes to image classification, the most used neural networks include Convolution Neural Networks (CNN). It contains various convolution layers which are responsible for the extraction of important features from the image. The earlier layers are responsible for low-level details and the later layers are completely responsible for more high-level features.
The Convolution operation uses a custom matrix, popular as filters. It convolutes over the input image and produce maps. These filters are initialized randomly by the students of Artificial Intelligence college in Jaipur and then are updated via back propagation. For instance, the Canny Edge Detector filter used to find the edges in any image.
Recurrent Neural Networks
Recurrent Neural Networks are considered when there is a need for predictions using sequential data. Sequential data can be a sequence of images, words, or more. The RNN have a similar structure to a Feed-Forward Network. This instance prediction stored in the RNN cell by the students of the Best Engineering Colleges which is a second input for every prediction. However, the main drawback of RNN is the Vanishing Gradient problem. Which makes it very difficult to remember earlier layers’ weights.
Long Short-Term Memory Networks
LSTM neural networks overcome the problems of Vanishing Gradient in RNNs by adding a special memory cell. Also, it can store information for long periods of time. LSTM uses gates to define which output should be used or forgotten easily. It uses 3 gates including Input gate, Output gate and a Forget gate. The Input gate controls how data is kept in memory. The Output gate controls the data provided to the next layer and the forget gate controls when to dump/forget the data not required.
LSTMs used in various applications by the students of btech colleges Jaipur like Gesture recognition, Speech recognition, Text prediction, etc.
Neural Networks are very complex within no time and help students of top private engineering college keep on adding layers in the network. In some cases, they can leverage the immense research in this field by using pre-trained networks for our use. This known as Transfer Learning.