From 625bb4416d60f3b6a9e0dee9d53b3c42f4c93245 Mon Sep 17 00:00:00 2001
From: resrv <webgivers@gmail.com>
Date: Fri, 28 Jun 2019 03:49:05 +0530
Subject: [PATCH] Added RNN (#29276)

---
 .../recurrent-neural-networks/index.md           | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/guide/english/machine-learning/neural-networks/recurrent-neural-networks/index.md b/guide/english/machine-learning/neural-networks/recurrent-neural-networks/index.md
index af4bfa06b3..d6545b9987 100644
--- a/guide/english/machine-learning/neural-networks/recurrent-neural-networks/index.md
+++ b/guide/english/machine-learning/neural-networks/recurrent-neural-networks/index.md
@@ -3,13 +3,13 @@ title: Recurrent Neural Networks
 ---
 ## Recurrent Neural Networks
 
-This is a stub. <a href='https://github.com/freecodecamp/guides/tree/master/src/pages/machine-learning/neural-networks/recurrent-neural-networks/index.md' target='_blank' rel='nofollow'>Help our community expand it</a>.
-
-<a href='https://github.com/freecodecamp/guides/blob/master/README.md' target='_blank' rel='nofollow'>This quick style guide will help ensure your pull request gets accepted</a>.
-
-<!-- The article goes here, in GitHub-flavored Markdown. Feel free to add YouTube videos, images, and CodePen/JSBin embeds  -->
-
-#### More Information:
-<!-- Please add any articles you think might be helpful to read before writing the article -->
+Recurrent Neural Networks (RNN) are a category of Neural Networks which are mainly used for data which are sequential in nature. In Recurrent Neural Networks, the output of the previous time is fed as an input to the current time. RNN's have been extensively used for Natural Language Processing, Time Series Data prediction and other tasks which involve sequential data.
 
+### Types of Recurrent Neural Networks
+* Long Short Term Memory (LSTM)
+* Gated Recurrent Unit (GRU)
 
+## Suggested Links
+* Stanford CS231 lesson 10 video https://www.youtube.com/watch?v=6niqTuYFZLQ
+* Deep Learning Book https://www.youtube.com/watch?v=6niqTuYFZLQ
+* Colah's blog http://colah.github.io/posts/2015-08-Understanding-LSTMs/