Unleashing Recurrent Neural Networks (RNN): Exploring Applications in AI/ML

Introduction to Recurrent neural network

In some cases, such as the previous words of a phrase must be recalled to predict the subsequent word, so they must be remembered. As a result, an RNN with a hidden layer was developed to solve the problem. The most crucial part of an RNN is the hidden state, which retains specific information about a sequence.

Simple Architecture of Recurrent Neural Network

Let X1 As input and 1 hidden layer, both module and Y1 and Y2 be the output of two models. The first module is normal feedforward neural network, and the second one is RNN

The first module is simple, taking input and giving output.

y1=f(w2(X1*w1+b1)+b2)

where f -s sigmoid, tanh, ReLu

The second module is a loop where the output of the hidden feedback loop is sent back to the hidden layer, allowing information to be passed from the output of the hidden layer by sending the previous value back to it, acting like a memory network.

Y= f(w3(X1*w1+b1)+w1X1+b)

Types of Recurrent Neural Networks

1. One-to-One

It is known as simple neural networks. It works with a fixed size input to a fixed size output, where neither depends on the other’s past data or output. The most effective example of this kind of RNN is image recognition.

2. One-to-Many

It works with fixed-size information as input and outputs a series of data. An appropriate example might be image captioning, which accepts an image as input and outputs a string of words.

3. Many-to-One

It produces a fixed-size output after receiving a sequence of data as input. It is employed, for instance, in sentiment analysis, which determines if a text expresses a positive or negative attitude.

4. Many-to-Many

This particular RNN repeatedly processes the output as a data sequence after taking in a sequence of information as input. RNNs read texts in one language and produce output in another as part of machine translation.

Why is a Recurrent Neural Network used for stock predictions?

Imagine the situation where you bought two different stocks. Stock A and B, and you must predict the future outcome.

Stock A was launched in 2012, and stock B was launched in 2020

In this scenario, a recurrent neural network is employed. If we need to develop a module that can forecast stocks A and B, we must consider prior data points. A typical neural network with backpropagation cannot store the prior data point. As a result, it cannot accurately forecast a future data point using the data. Whereas the recurrent neural network can store the value temporally and could give high accurse predictions using previous output by storing it.

Unrolling Recurrent neural network

Regardless of how often we unroll a recurrent neural network, weights, and biases are shared across every input. Meaning even though this unroll has 4 inputs, the weight W1 and W2, and B (Biases)

Struggle to learn long-term dependencies

One big problem is that the more we unroll recurrent networks, the harder it is to train

We call it the vanishing or exploding Gradient problem

When we combine the gradient descent approach with backpropagation, we can identify parameter values that reduce a loss function, such as the sum of squared residuals.

If we set W2 to a value greater than 1 and more, we unroll RNN, leading to an exploding gradient. For example, we W2=2

Now input X1 will multiply by W2 4 times in this example means

X1*2^N where N is the number of times in unroll

Because of it, we wouldn’t be able to find global minima using the gradient descendent algorithm

Conclusion

• Traditional feedforward algorithms cannot solve time-series and data sequence problems, whereas RNNs can do so efficiently.
• Recurrent Neural Networks are versatile tools used in various situations. They are used in several methods for language modeling and text generation. They are also used in speech recognition.
• When combined with Convolutional Neural Networks, this type of neural network generates labels for untagged images. This combination works incredibly well.
• However, recurrent neural networks have one flaw. They struggle to learn long-term dependencies, so they don’t understand relationships between data separated by multiple steps.

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