Deep Learning Networks
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Types of Deep Learning Networks

by Eric
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Deep learning is gaining immense popularity and is on the rage. This is majorly due to its supremacy in terms of accuracy. Major tech companies have now started to invest heavily in deep learning because they have realized that deep learning is essential in every sector to make machines more intelligent. Big tech companies are using deep learning to make advanced machine learning applications. An example can be Google which has used it in its search engine and developed AlphaGo and Smart Reply. Voice recognition and self-driving cars are also a result of deep learning only. Netflix’s movie or show recommendation, Facebook’s face recognition, YouTube’s thumbnail feature are all results of deep learning. So let’s examine this powerful concept in detail. To get a deeper and better understanding, you can opt for any free deep learning course online. 

What is deep learning?

Deep learning is computer software and a subset of machine learning that mimics the neuron networks in the human brain. It is based on artificial neural networks and utilizes them at a multi-layered hierarchical level to carry out representative learning. It uses deep neural networks to carry out complex processes of machine learning such as speech recognition, language translation, object detection with high accuracy. 

The learning in deep learning can be unsupervised, semi-supervised, or supervised. The algorithms in deep learning are constructed using connected layers. The first layer and the last layer are known as Input Layer and Output Layer, respectively, whereas all the layers in between are known as hidden layers. As the name suggests, deep learning consists of neuron networks joined in more than two layers.

The hierarchy in this system enables machines to use a non-linear approach in processing data. The neuron nodes in deep learning are connected like a web. In the first level, the networks learn something very simple and then transfer it to the next level. In the next level in the hierarchy, that simple information is further combined with something to make it more complex, and then it passes on to the next level. The complexity of the information keeps increasing with the hierarchy of the level. This helps the machine to automatically learn to translate and extract features for several data sets such as video, images, or text, without the introduction of traditional rules or code. 

Deep learning usually has two phases:

  • In the first phase, a nonlinear transformation of the input is applied to create an output in the form of a statistical model.
  • In the second phase, attention is given to improving the model using a mathematical method which is known as a derivative.

These two levels are repeated by the neural networks a hundred or thousand times until clear, or a tolerable limit of accuracy is achieved. This repetition of these two phases is known as ‘iteration.’

Types of Deep Learning Networks

There are essentially 7 types of deep learning networks. Let’s look into each one of them one by one:

  • Feedforward neural network

This is a very basic neural network in which the flow of control starts from the input layer and moves towards the output layer. These networks have single layers, i.e.only one hidden layer. Since the data is moving only in one direction in this network, there are no backpropagation techniques. This kind of network is used in computer vision for facial recognition algorithms. The input in the input layer is usually the sum of weights present.

  • Radial basis function neural networks

This type of neural network has preferably more than one layer, most commonly only two layers. In this, the relative distance of any point from the center is calculated and is passed on to the next layer. These neural networks are mostly used in power restoration systems which restore power in a short amount of time to avoid blackouts.

  • Multi-layer perceptron

These neural networks are used for classifying non-linear data and have more than three layers. These networks are completely connected with every node. They are most commonly used in machine learning technologies such as speech recognition.

  • Convolution neural network (CNN)

Convolution neural network is a variant of the multi-layer perceptron. It can contain more than one convolution layer, making the network very deep with only a few parameters. It is extensively used in image recognition and identifying image patterns.

  • Recurrent Neural Network (RNN)

A recurrent neural network is a network in which the output of a neuron is entered back as an input into the same node. This network highly assists the network in predicting more accurate outputs. This kind of network is used to maintain small memory states used in developing technologies like chatbots. Apart from the development of chatbots, this technology is also used in text-to-speech programs. 

  • Modular Neural Network

The modular neural network is not just a single network but a collaboration of various small neural networks. All of these sub-networks combine to make a big neural network in which all the networks work independently to achieve a common target. These networks are highly effective in breaking large or big problems into smaller parts and then solving them.

  • Sequence to sequence models

These types of networks are mostly a combination of two recurrent neural networks (RNN). This network relies on encoding and decoding. Therefore, it consists of an encoder used for processing the input and a decoder used for processing the output. This network is used in things like text processing, where the entered or inputted text is lengthier than the outputted text.

Conclusion

Deep learning is used across industries for a variety of different tasks. Deep learning also has significant and huge applications in our daily life and all activities. It makes it easier for us to perform different tasks and makes big data jobs convenient. It mimics the human brain to use it in decision-making processes by processing data. It can be used to detect fraud, money laundering, and many more important tasks. This article has briefly explained what deep learning is and what are the different types of deep learning networks. If you want a deeper understanding, you can always opt for a deep learning course or a certification.

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