Deep Learning - Computer Vision for Beginners Using PyTorch - First CNN

The video tutorial covers the creation and execution of a Convolutional Neural Network (CNN) using a custom module. It begins with setting up the environment and executing initial code to load image batches. The instructor then demonstrates how to create a CNN class, instantiate it, and execute convolution operations. The video also explains how to visualize the output of these operations and discusses the impact of padding and stride on image dimensions. The tutorial concludes with a summary of the CNN operations covered.

What is the purpose of setting the batch size to 4 in the CNN class?

• To process four images simultaneously

• To enhance the image resolution

• To increase the number of channels

• To reduce the kernel size

In the CNN class, what does the parameter '3' signify in the convolution operation?

• Number of output channels

• Kernel size

• Number of input channels

• Stride value

What does the '10' represent in the output tensor shape 4x10x30x30?

• Number of output channels

• Number of input images

• Stride value

• Kernel size

Why is there a reduction in image size from 32x32 to 30x30 in the output tensor?

• Due to the absence of padding

• Due to the use of padding

• Because of the kernel size

• Because of the stride value

What is the correct method to convert a tensor to a numpy array when it requires gradients?

• Use tensor.numpy().detach()

• Use tensor.grad().numpy()

• Use tensor.detach().numpy()

• Use tensor.numpy() directly

How does adding padding of (1,1) affect the output image dimensions?

• Doubles the dimensions

• Keeps the dimensions unchanged

• Decreases the dimensions

• Increases the dimensions

What happens to the output image dimensions when the stride is set to (2,2) without padding?

• Dimensions are doubled

• Dimensions are halved

• Dimensions remain the same

• Dimensions are increased by one