What's the difference between convolutional and recurrent neural networks? [closed]

Answer 1

Difference between CNN and RNN are as follows:

CNN:

1. CNN takes a fixed size inputs and generates fixed-size outputs.

2. CNN is a type of feed-forward artificial neural network - are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing.

3. CNNs use connectivity pattern between its neurons and 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.

4. CNNs are ideal for images and video processing.

RNN:

1. RNN can handle arbitrary input/output lengths.

2. RNN unlike feedforward neural networks - can use their internal memory to process arbitrary sequences of inputs.

3. Recurrent neural networks use time-series information. i.e. what I spoke last will impact what I will speak next.

4. RNNs are ideal for text and speech analysis.

Answer 2

This is the difference between CNN and RNN

Convolutional Neural NEtwork:

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. ... They have applications in image and video recognition, recommender systems, image classification, medical image analysis, and natural language processing.

Recurrent Neural Networks:

A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.