What is the difference between a Residual Neural Net and a Recurrent Neural Net?

As I understand,

Residual Neural Networks are very deep networks that implement 'shortcut' connections across multiple layers in order to preserve context and eliminate exploding/vanishing gradients. Layers in a residual neural net have input from the layer before it and the optional, less processed data, from X layers higher. This prevents early or deep layers from 'dieing' due to vanishing/exploding gradients.

Recurrent Neural Networks are networks that contain a directed cycle which when computed is 'unrolled' through time. Layers in recurrent neural network have input from the layer before it and the optional, time dependent extra input. This provides situational context for things like natural language processing.

Therefor, a recurrent neural network can be used to generate a basic residual network if the input remains the same with respect to time.

Is this correct?

What is the difference between a Residual Neural Net and a Recurrent Neural Net?

As I understand,

Residual Neural Networks are very deep networks that implement 'shortcut' connections across multiple layers in order to preserve context as depth increases. Layers in a residual neural net have input from the layer before it and the optional, less processed data, from X layers higher. This prevents early or deep layers from 'dying' due to converging loss.

Recurrent Neural Networks are networks that contain a directed cycle which when computed is 'unrolled' through time. Layers in recurrent neural network have input from the layer before it and the optional, time dependent extra input. This provides situational context for things like natural language processing.

Therefor, a recurrent neural network can be used to generate a basic residual network if the input remains the same with respect to time.

Is this correct?

What is the difference between a Residual Neural Net and a Recurrent Neural Net?

As I understand,

Residual Neural Networks are very deep networks that implement 'shortcut' connections across multiple layers in order to preserve context and eliminate exploding/vanishing gradients.

Recurrent Neural Networks are networks that contain a directed cycle which when computed is 'unrolled' through time essentially creating a residual neural network.

Am I missing something?

What is the difference between a Residual Neural Net and a Recurrent Neural Net?

As I understand,

Residual Neural Networks are very deep networks that implement 'shortcut' connections across multiple layers in order to preserve context and eliminate exploding/vanishing gradients. Layers in a residual neural net have input from the layer before it and the optional, less processed data, from X layers higher. This prevents early or deep layers from 'dieing' due to vanishing/exploding gradients.

Recurrent Neural Networks are networks that contain a directed cycle which when computed is 'unrolled' through time. Layers in recurrent neural network have input from the layer before it and the optional, time dependent extra input. This provides situational context for things like natural language processing.

Therefor, a recurrent neural network can be used to generate a basic residual network if the input remains the same with respect to time.

Is this correct?