- TensorFlow Tutorial
- TensorFlow - Home
- TensorFlow - Introduction
- TensorFlow - Installation
- Understanding Artificial Intelligence
- Mathematical Foundations
- Machine Learning & Deep Learning
- TensorFlow - Basics
- Convolutional Neural Networks
- Recurrent Neural Networks
- TensorBoard Visualization
- TensorFlow - Word Embedding
- Single Layer Perceptron
- TensorFlow - Linear Regression
- TFLearn and its installation
- CNN and RNN Difference
- TensorFlow - Keras
- TensorFlow - Distributed Computing
- TensorFlow - Exporting
- Multi-Layer Perceptron Learning
- Hidden Layers of Perceptron
- TensorFlow - Optimizers
- TensorFlow - XOR Implementation
- Gradient Descent Optimization
- TensorFlow - Forming Graphs
- Image Recognition using TensorFlow
- Recommendations for Neural Network Training
- TensorFlow Useful Resources
- TensorFlow - Quick Guide
- TensorFlow - Useful Resources
- TensorFlow - Discussion
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
TensorFlow - CNN And RNN Difference
In this chapter, we will focus on the difference between CNN and RNN −
| CNN | RNN |
|---|---|
| It is suitable for spatial data such as images. | RNN is suitable for temporal data, also called sequential data. |
| CNN is considered to be more powerful than RNN. | RNN includes less feature compatibility when compared to CNN. |
| This network takes fixed size inputs and generates fixed size outputs. | RNN can handle arbitrary input/output lengths. |
| CNN is a type of feed-forward artificial neural network with variations of multilayer perceptrons designed to use minimal amounts of preprocessing. | RNN unlike feed forward neural networks - can use their internal memory to process arbitrary sequences of inputs. |
| CNNs use connectivity pattern between the neurons. This is inspired by the organization of the animal visual cortex, whose individual neurons are arranged in such a way that they respond to overlapping regions tiling the visual field. | Recurrent neural networks use time-series information - what a user spoke last will impact what he/she will speak next. |
| CNNs are ideal for images and video processing. | RNNs are ideal for text and speech analysis. |
Following illustration shows the schematic representation of CNN and RNN −
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