Different Types of Neural Networks in Deep Learning - CNN, RNN, ANN

NLP-enabled solutions are increasingly being used by businesses to derive insights from data and automate mundane processes.

Natural Language Processing (NLP) is a branch of artificial intelligence that allows machines to understand human speech. Its purpose is to create systems that can understand text and conduct activities like translation, spell check, and topic classification automatically.

What is Deep Learning?

It's an interesting question. Why should a data scientist lean toward deep learning algorithms when there are many machine learning algorithms to choose from? What are the advantages of neural networks over classic machine learning algorithms?

Another typical concern is whether or not it is worthwhile to use neural networks because they need a lot of computing resources. While there's a lot of nuance in that question, the quick answer is yes!

Convolutional neural networks (CNN), recurrent neural networks (RNN), artificial neural networks (ANN), and other forms of deep learning neural networks are transforming the way we interact with the world. These various neural networks are at the heart of the deep learning revolution, fueling applications like unmanned aerial vehicles, self-driving automobiles, and voice recognition, among others.

What exactly is an ANN, and why should you care about it?

A single perceptron can be compared to a Logistic Regression. On every layer of an Artificial Neural Network or ANN, there are many perceptrons/neurons. Because inputs are exclusively processed in the forward direction, an ANN is also known as a Feed-Forward Neural Network.

Input, Hidden, and Output are the three levels of an ANN. The input layer receives the data, the hidden layer processes it, and the output layer generates the output. Each layer is essentially attempting to learn certain weights.

An Artificial Neural Network can learn any nonlinear function.

As a result, these networks are commonly referred to as Universal Function Approximators. ANNs can learn weights that map any input to the desired output.

RNN – What is a Recurrent Neural Network and why should you use one?

While making predictions, RNN captures the sequential information available in the input data, i.e., the dependency between the words in the text. The parameters of RNNs are shared between time steps. This is commonly referred to as parameter sharing. As a result, there are fewer parameters to train, lowering the computational cost.

CNN (Convolutional Neural Network) - What is a CNN and why would you want to use one?

In the deep learning community, convolutional neural networks (CNN) are all the rage right now. These CNN models are employed in a variety of applications and domains, but they're particularly common in image and video processing projects.

Filters, often known as kernels, are the building components of CNNs. The convolution technique is used to extract meaningful information from the input using kernels.

End Notes

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