Fundamentals Revisited

Question #1 CNN - Why Convolution 2D?

2D convolution refers *not* to the dimension of the convolution kernel but to the dimension of the output. The output’s dimension is 2D, single channel. A single 2D convolution pass over a 3D image uses a 3D convolution kernel to obtain the 2D output

Conv2D: Input-->One filter --> 2D output

Conv3D: Input--> One filter --> 3D output

Ref - Link

Question #2 - Visualize Layers

The output across multiple layers. Link

Question #3 - Different types of Data Augmentation

Ref - Link

Keep Exploring!!!