The main reason that the recurrent nets are more exciting is that they allow us to operate over sequences of vectors: Sequence in the input, the output, or in the most general case, both. A few examples may this more concrete:

Each rectangle in the above image represents vectors, and arrows represent functions. Input vectors are Red, output vectors are blue, and green holds RNN’s state.
One-to-one:
This is also called Plain Neural networks. It deals with a fixed size of the input to the fixed size of output, where they are independent of previous information/output.
Example: Image classification.
One-to-Many:
It deals with a fixed size of information as input that gives a sequence of data as output.
Example: Image Captioning takes the image as input and outputs a sentence of words.
Many-to-One:
It takes a sequence of information as input and outputs a fixed size of the output.
Example: sentiment analysis where any sentence is classified as expressing the positive or negative sentiment.
Many-to-Many:
It takes a Sequence of information as input and processes the recurrently outputs as a Sequence of data.
Example: Machine Translation, where the RNN reads any sentence in English and then outputs the sentence in French.
Bidirectional Many-to-Many:
Synced sequence input and output. Notice that in every case are no pre-specified constraints on the lengths sequences because the recurrent transformation (green) is fixed and can be applied as many times as we like.
Example: Video classification where we wish to label every frame of the video.

Advantages of Recurrent Neural Network

• RNN can model a sequence of data so that each sample can be assumed to be dependent on previous ones.
• A recurrent neural network is even used with convolutional layers to extend the active pixel neighborhood.

Disadvantages of Recurrent Neural Network

• Gradient vanishing and exploding problems.
• Training an RNN is a complicated task.
• It could not process very long sequences if it were using tanh or relu like an activation function.

Difference between CNN and RNN

S.no	CNN	RNN
1	CNN stands for Convolutional Neural Network.	RNN stands for Recurrent Neural Network.
2	CNN is considered to be more potent than RNN.	RNN includes less feature compatibility when compared to CNN.
3	CNN is ideal for images and video processing.	RNN is ideal for text and speech Analysis.
4	It is suitable for spatial data like images.	RNN is used for temporal data, also called sequential data.
5	The network takes fixed-size inputs and generates fixed size outputs.	RNN can handle arbitrary input/ output lengths.
6	CNN is a type of feed-forward artificial neural network with variations of multilayer perceptron’s designed to use minimal amounts of preprocessing.	RNN, unlike feed-forward neural networks- can use their internal memory to process arbitrary sequences of inputs.
7	CNN’s use of connectivity patterns between the neurons. CNN is affected by the organization of the animal visual cortex, whose individual neurons are arranged in such a way that they can respond to overlapping regions in the visual field.	Recurrent neural networks use time-series information- what a user spoke last would impact what he will speak next.

Following are the diagram shows the schematic representation of CNN and RNN

So, this brings us to the end of blog. This Tecklearn ‘Types of RNN and CNN Vs RNN’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:
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Introduction to Deep Learning and AI
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning
• Real-Life use cases of Deep Learning
• Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
• Pre-requisites for AI & DL
• Python Programming Language
• Installation & IDE
Environment Set Up and Essentials
• Installation
• Python – NumPy
• Python for Data Science and AI
• Python Language Essentials
• Python Libraries – Numpy and Pandas
• Numpy for Mathematical Computing
More Prerequisites for Deep Learning and AI
• Pandas for Data Analysis
• Machine Learning Basic Concepts
• Normalization
• Data Set
• Machine Learning Concepts
• Regression
• Logistic Regression
• SVM – Support Vector Machines
• Decision Trees
• Python Libraries for Data Science and AI
Introduction to Neural Networks
• Creating Module
• Neural Network Equation
• Sigmoid Function
• Multi-layered perception
• Weights, Biases
• Activation Functions
• Gradient Decent or Error function
• Epoch, Forward & backword propagation
• What is TensorFlow?
• TensorFlow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
Multi-layered Neural Networks
• Error Back propagation issues
• Drop outs
Regularization techniques in Deep Learning
Deep Learning Libraries
• Tensorflow
• Keras
• OpenCV
• SkImage
• PIL
Building of Simple Neural Network from Scratch from Simple Equation
• Training the model
Dual Equation Neural Network
• TensorFlow
• Predicting Algorithm
Introduction to Keras API
• Define Keras
• How to compose Models in Keras
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• What is Batch Normalization
• Saving and Loading a model with Keras
• Customizing the Training Process
• Using TensorBoard with Keras
• Use-Case Implementation with Keras
GPU in Deep Learning
• Introduction to GPUs and how they differ from CPUs
• Importance of GPUs in training Deep Learning Networks
• The GPU constituent with simpler core and concurrent hardware
• Keras Model Saving and Reusing
• Deploying Keras with TensorBoard
Keras Cat Vs Dog Modelling
• Activation Functions in Neural Network
Optimization Techniques
• Some Examples for Neural Network
Convolutional Neural Networks (CNN)
• Introduction to CNNs
• CNNs Application
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• Understanding and Visualizing a CNN
RNN: Recurrent Neural Networks
• Introduction to RNN Model
• Application use cases of RNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model
Application of Deep Learning in image recognition, NLP and more
Real world projects in recommender systems and others
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