How Many Types of deep learning Networks are there?
Deep learning networks are mathematical models that are used to imitate the human brain as it is to solve problems using unstructured data, these mathematical models are built in the form of neural networks that contain neurons. Neural networks are divided into three major layers which are the input layer (the first layer of the neural network), the hidden layer (all the middle layers of the neural network), and the output layer (the last layer of the neural network). Based on this type of data we will deal with these neural networks which are classified as feed-forward neural networks, CNNs, RNNs, modular neural networks, etc.
Now let us know how many types of deep learning networks are over there?
No.1 radial basis function neural network: Such neural networks typically have more than 1 layer, preferably two layers. In such a network, the relative distance from any point to the center is calculated and passed on to the next layer. Radial base networks are commonly used in power restoration systems to restore power in the shortest possible time to avoid blackouts.
No.2 multi-layer perceptron networks: This type of network are having more than 3 layers and its used to classify the data which is not linear. These kinds of networks are fully connected with every node. These networks are extensively used for speech recognition and other machine learning technologies.
No3. modular neural network: Such a network is not a single network but a combination of several smaller neural networks. All the sub-networks form a larger neural network and they all act independently to achieve a common goal.
These networks are very helpful in breaking down a big problem into small pieces and then solving it.
No4. The sequence of Sequence Model network: This type of network is generally a combination of two RNN networks. The network works on encoding and decoding i.e. it consists of an encoder that is used to process the input and a decoder that is used to process the output. Typically, such networks are used for text processing, where the length of the input text is not the same as the output text.
No.5 recurrent neural network: RNN is a type of neural network where the output of a particular neuron is fed back as the input of the same node. This method helps the network to predict the output. This type of network is useful in maintaining a small state of memory which is very useful for developing chatbots.
Such networks are used in chatbot development and text-to-speech technology.
No.6 Convolution Neural Network (CNN): CNN is one of the variations of the multilayer perceptron. CNN can have more than 1 convolution layer and since it has one convolution layer, the network is very deep with fewer parameters. CNN is very effective for image recognition and identification of different image patterns.
No.7feedforward neural network: This type of neural network is a very basic neural network where the flow control is from the input layer and leads to the output layer. This type of network has only one layer or only 1 hidden layer. Since the data moves in only 1 direction, there is no backpropagation technique in this network. In this network, the sum of the weights present in the input is fed into the input layer. This type of network is used in facial recognition algorithms using computer vision.
Final words: In this article, we looked at what is meant by deep learning and what are all the different deep learning networks currently in use in the market. We have also seen the working of all those networks and the intricacies of the application of those networks.
If you are a deep learning student and want to boom your career as a deep learning engineer then, we would suggest you join NearLearn for deep Learning.
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