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Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data by maintaining a form of memory. Unlike traditional feedforward neural networks, RNNs can utilize information from previous inputs in the sequence, making them particularly effective for tasks such as natural language processing, time series prediction, and speech recognition. They achieve this by passing the output of the previous step as input to the current step, allowing the network to learn patterns over time. This unique architecture enables RNNs to capture temporal dependencies, making them a powerful tool in various applications involving sequential data.

What are Recurrent Neural Networks (RNN) ?

Becoming Human: Artificial Intelligence Magazine

An introduction to Recurrent Neural Networks (RNN) ?

The Simplest Interesting (and Useful) Recurrent Neural Network

Towards Data Science

A recurrent neural network (RNN) processes an input sequence arriving as a stream. It maintains state, i.e. memory. This captures whatever it has seen in the input to this point that it deems…

Explained: Recurrent Neural Networks

Analytics Vidhya

Recurrent Neural Networks are specialized neural networks designed specifically for data available in form of sequence. Few examples of sequence data could be text data such as tweets or comments…

Three Types of Recurrent Neural Networks

Towards AI

Recurrent Neural Networks are neural networks designed for sequence data. Sequence data is any data that comes in a form in which former data points affect later data points. RNNs can be applied to…

Implementing Recurrent Neural Network using Numpy

Towards Data Science

Recurrent neural network (RNN) is one of the earliest neural networks that was able to provide a break through in the field of NLP. The beauty of this network is its capacity to store memory of…

Getting Started with Recurrent Neural Network (RNNs)

Towards Data Science

Using RNNs for Sentiment Analysis Photo by Nishaan Ahmed from Unsplash This article will discuss a separate set of networks known as Recurrent Neural Networks(RNNs) built to solve sequence or time se...

An Introduction to Recurrent Neural Networks for Beginners

Towards Data Science

Recurrent Neural Networks (RNNs) are a kind of neural network that specialize in processing sequences. They’re often used in Natural Language Processing (NLP) tasks because of their effectiveness in…

Recurrent Neural Networks for Dummies

Towards AI

Recurrent Neural Networks(RNN) lies under the umbrella of Deep Learning. They are utilized in operations involving Natural Language Processing. Nowadays since the range of AI is expanding enormously…

Recurrent Neural Networks

Towards Data Science

The goal of this article is to explore Recurrent Neural Networks in-depth, which are a kind of Neural Networks with a different architecture than the ones seen in previous articles (Link). As we have…...

Recurrent Neural Networks, Explained and Visualized from the Ground Up

Towards Data Science

Recurrent Neural Networks (RNNs) are neural networks that can operate sequentially. Although they’re not as popular as they were even just several years ago, they represent an important development…

The Basics of Recurrent Neural Networks (RNNs)

Towards AI

Recurrent Neural Networks (RNNs) are widely used for data with some kind of sequential structure. For instance, time series data has an intrinsic ordering based on time. Sentences are also…

The Intuition of Vanilla Recurrent Neural Networks

Level Up Coding

Recurrent Neural Networks are neural networks that are specialized in modeling sequence data. Essentially RNNs are designed to capture information from time sequences and time-series data. Well, we…