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What are the different types of deep neural networks?

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Following are the different types of deep neural networks:-

  1. FeedForward Neural Network:- This is the most basic type of neural network, in which flow control starts at the input layer and moves to the output layer. These networks only have a single layer or a single hidden layer. There is no backpropagation mechanism in this network because data only flows in one way. The input layer of this network receives the sum of the weights present in the input. These networks are utilised in the computer vision-based facial recognition method.
  2. Radial Basis Function Neural Network:- This type of neural network usually has more than one layer, preferably two. The relative distance from any location to the center is determined in this type of network and passed on to the next layer. In order to avoid blackouts, radial basis networks are commonly employed in power restoration systems to restore power in the shortest period possible.
  3. Multi-Layer Perceptrons (MLP):- A multilayer perceptron (MLP) is a type of feedforward artificial neural network (ANN). MLPs are the simplest deep neural networks, consisting of a succession of completely linked layers. Each successive layer is made up of a collection of nonlinear functions that are the weighted sum of all the previous layer's outputs (completely linked). Speech recognition and other machine learning systems rely heavily on these networks.
  4. Convolutional Neural Network (CNN):- Convolutional Neural Networks are mostly used in computer vision. In contrast to fully linked layers in MLPs, one or more convolution layers extract simple characteristics from input by performing convolution operations in CNN models. Each layer is made up of nonlinear functions of weighted sums at various coordinates of spatially close subsets of the previous layer's outputs, allowing the weights to be reused.
  5. The AI system learns to automatically extract the properties of these inputs to fulfill a specific task, such as picture classification, face identification, and image semantic segmentation, given a sequence of images or videos from the actual world.
  6. Recurrent Neural Network (RNN):- Recurrent Neural Networks were created to solve the sequential input data time-series problem. RNN's input is made up of the current input and prior samples. As a result, the node connections create a directed graph. Furthermore, each neuron in an RNN has an internal memory that stores the information from previous samples' computations. Because of their superiority in processing data with a variable input length, RNN models are commonly employed in natural language processing (NLP). The goal of AI in this case is to create a system that can understand human-spoken natural languages, such as natural language modeling, word embedding, and machine translation.
  7. Each successive layer in an RNN is made up of nonlinear functions of weighted sums of outputs and the preceding state. As a result, the basic unit of RNN is termed "cell," and each cell is made up of layers and a succession of cells that allow recurrent neural network models to be processed sequentially.
  8. Modular Neural Network:- This network is made up of numerous tiny neural networks, rather than being a single network. The sub-networks combine to form a larger neural network, which operates independently to achieve a common goal. These networks are extremely useful for breaking down a large-small problem into smaller chunks and then solving it.
  9. Sequence to Sequence Model:-  In most cases, this network is made up of two RNN networks. The network is based on encoding and decoding, which means it has an encoder that processes the input and a decoder that processes the output. This type of network is commonly employed for text processing when the length of the inputting text differs from the length of the outputted text.

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