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## What is 3D CNNs?

A 3D CNN is simply the 3D equivalent: it takes as input a 3D volume or a sequence of 2D frames (e.g. slices in a CT scan), 3D CNNs are a powerful model for learning representations for volumetric data.

## What is Conv1D?

We can see that the 2D in Conv2D means each channel in the input and filter is 2 dimensional(as we see in the gif example) and 1D in Conv1D means each channel in the input and filter is 1 dimensional(as we see in the cat and dog NLP example).

## How does 3D CNN works?

3D convolutions applies a 3 dimentional filter to the dataset and the filter moves 3-direction (x, y, z) to calcuate the low level feature representations. Their output shape is a 3 dimentional volume space such as cube or cuboid. They are helpful in event detection in videos, 3D medical images etc.

## What’s the difference between 1D, 2D and 3D convolutions?

Can anyone please clearly explain the difference between 1D, 2D, and 3D convolutions in convolutional neural networks (in deep learning) with the use of examples? I want to explain with picture from C3D.

## How is 2D convolution used in deep learning?

2D convolution is very prevalent in the realm of deep learning. CNNs (Convolution Neural Networks) use 2D convolution operation for almost all computer vision tasks (e.g. Image classification, object detection, video classification). 3D Convolution

## Can a 2D Conv be used with 3D input?

The 2d conv with 3d input is a nice touch. I would suggest an edit to include 1d conv with 2d input (e.g. a multi-channel array) and compare the difference thereof with a 2d conv with 2d input. Great Explanation. Amazing answer!

## What’s the difference between a 2D and a 3D figure?

A 3D figure has length, width and height in the x-axis, y-axis and z-axis respectively. Unlike 2D figures, 3D figures exist beyond the margins of a flat or plane surface, they have a defining depth to their structure which extends to a new dimension called the z-axis. This added axis is to deter the height of the figure.