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What do you mean by Neural Network?

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21-July-2022 Deep Learning

What do you mean by Neural Network?

But before moving deep down to the concept of Recurrent Neural Networks (RNNs), let us learn about the neural network and deep learning.

What do you mean by Neural Network?

Recurrent Neural Network (RNNs)

In this tutorial, we will discover the Recurrent Neural network (RNNs), which is considered the most important type of learning to achieve deep learning.

But before moving deep down to the concept of Recurrent Neural Networks (RNNs), let us learn about the neural network and deep learning.

What do you mean by Neural Network?

Neural Network, also termed Artificial Neural networks (ANNs), or we can call the simulated neural networks (SNNs), which in turn, is the most important subset of Machine Learning and are the heart of deep learning algorithms.

Moreover, the name neural network is primarily derived from the structure of the normal human brain, in same as the neurons are connected to better functioning of the human brain.

The Neural Network or the Artificial Neural Network (ANNs) combines the effective node layers, which contain the input layer, one or more hidden layers, and an output layer. In this network, each node gets connected with the other nodes and has an associated threshold and weight. And in these, if the output related to the individual node is just above the determined threshold value, the node automatically gets activated. It sends a particular amount of data to the next layer of the network. If the output is related to the individual node is less than the determined threshold value, then the node will not be activated to send data to the next layer in the network.

What do you understand about Deep Learning?

Deep Learning is considered one of the most common concepts in the modern computer world. Deep learning can be defined as the important part (subset) of Machine Learning, that is, eventually, a neural network encapsulated with three or more layers as this respective neural network helps effectively simulate the behaviour related to the human brain. Whereas the neural network with a combination of single-layer can also help in the predictions (approximate), the additional hidden layers will also help optimize and refine the accuracy to a greater extent.

 For a better understanding of deep learning, one can imagine the real-life example of situation when we are kids and are asked to learn the respective alphabets of English from the books that contain colourful images related to the alphabet that is for the letter "A" image of Apple and same rest different images for the other English alphabets, as these images help an individual to learn perfectly and fastly. They can remember for longer same in case of watching movies the dialogues get by-hearted and live for long-lasting as comparison with getting learned forcefully without any images or we can say that without any visuals.

What do you mean by Recurrent Neural Network?        

A Recurrent Neural Network (RNN) is considered the finest type of Artificial Neural Network, which is used for speech recognition and the natural processing of Language. And Recurrent Neural networks (RNNs) make recognitions of data's sequential characteristics and use the particular patterns to make predictions about the next occurring scenario.

Recurrent Neural networks (RNNs) are used in deep learning to a greater extent. They are also used to effectively develop models that simulate the activity of the neurons in the human brain.

Furthermore, the Recurrent Neural Network (RNNs) has” memory” that, in turn, remembers all the information containing all the calculations performed. And for these things, the recurrent neural network (RNNs) uses similar parameters for every individual input as they are liable to perform the same task on every input layer and to the hidden layers to produce the output. The major benefit of using these is that it reduces the parameter complexity compared to other neural networks.

Various Applications related to the RNNs.

The different applications that make use of the Recurrent Neural networks (RNNs) are as follows;

  1. Captioning of Image: One of the most important applications of the recurrent neural network (RNNs) is to caption a particular image to help analyze the activities that are present at that moment.
  2. Time-series Prediction: Another most important application of the respective recurrent neural network (RNNs) is the time-series predication, as in any problem related to the time series, such as predicting the prices related to the stocks for the respective month, can be very easily solved with the effective help of the recurrent neural network (RNNs).
  3. Natural Processing of Language:  Natural processing of Language is another most important principle of the particular recurrent neural network (RNNs), which means the mining of the particular amount of the text and the sentiments analysis can be achieved very smoothly with the help of the Recurrent Neural Network (RNNs).
  4. Machine Translation: Machine Translation is the other most important application of the Recurrent Neural networks (RNNs). In these given inputs in one respective Language, the RNNs can easily translate the input into the different languages in the form of the output efficiently. 

Advantages of Recurrent Neural Network

The various important advantages related to Recurrent Neural networks (RNNs) are as follows:

  • The most important advantage of the recurrent neural network is that it remembers each piece of the associated information through the respective time. They are very useful in time-series prediction because they are good enough to remember the previous inputs very well, termed the Short-Term Long Memory. 
  • The other most important advantage of the recurrent neural network is that it can be easily used with particular convolutional layers to extend the effective pixel neighbourhood.

Disadvantages of Recurrent Neural Network

There are various amounts of disadvantages that are associated with the recurrent neural network (RNNs), which are as follows:

  • One of the important disadvantages of the recurrent neural network (RNNs) is that they are also associated with the problems of exploding and the gradient vanishing.
  • Another most important disadvantage linked with the recurrent neural network is the training related to the RNNs is quite a difficult task.
  • If an individual uses tanh or relu to be the activation functions, then RNNs (recurrent neural networks) cannot process the long sequence.

 

 

 

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