What are Recurrent Neural Networks (RNN) ?
An introduction to Recurrent neural networks
In the previous article I have talked a lot about deep learning, neural networks, and types of them but today in this article we will learn about RNN a type of neural network in deep learning
what is a neural network?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. neural networks refer to systems of neurons.
the neural network is also a set of organized neurons .these neural networks have many layers between the input and output layers which and the layers between them are hidden.
what is a neuron?
a neuron is a mathematical function each neuron take an input and process the particular information and returns the output.
where x1,x2,x3 are input and w1,w2,w3 are their weights and we club them to into a neuron and y is the output given by the neuron
What is a Recurrent Neural Network (RNN)?
A recurrent neural network is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior.
Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable-length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition, or speech recognition. Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.
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Feed-Forward Neural Networks
A feed-forward neural network allows information to flow only in the forward direction, from the input nodes, through the hidden layers, and to the output nodes. There are no cycles or loops in the network.
How Recurrent Neural Networks works ?
In a RNN the information cycles through a loop. When it makes a decision, it considers the current input and also what it has learned from the inputs it received previously. The loops in RNN ensure the information is preserved in its memory.
In a feed-forward neural network, the information can move in one direction only. i.e., from the input layer to the hidden layer and then to the output layer. You need to note that once the information passes, it moves in a straight direction, and no node is touched upon for a second time.Types of Recurrent Neural Networks
There are four types of Recurrent Neural Networks:
One to One
This type of neural network is known as the Vanilla Neural Network. It’s used for general machine learning problems, which has a single input and a single output. These is also called Plain Neural networks.
One to Many
This type of neural network has a single input and multiple outputs. An example of this is the image caption.
Many to One
This RNN takes a sequence of inputs and generates a single output. Sentiment analysis is a good example of this kind of network where a given sentence can be classified as expressing positive or negative sentiments.
Many to Many
This RNN takes a sequence of inputs and generates a sequence of outputs. Machine translation is one of the examples
Applications of Recurrent Neural Networks
- Image Captioning
- Time Series Prediction
- Natural Language Processing
Further links
to know more about deep learning you can click on the link
https://becominghuman.ai/what-is-deep-learning-f441713ffb3c
to know more about neural networks you can click on the link
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