Deep Learning CNN Convolutional Neural Networks with Python - Extending to Multiple Layers Solution

The video tutorial explains the concept of trainable parameters in neural network layers, emphasizing the impact of increasing parameters on computational cost. It covers the input layer, first convolution layer with kernel size and padding, dropout layer, second convolution layer, max pooling, and third convolution layer. The tutorial provides formulas for calculating parameters and highlights the role of each layer in the network architecture.

Why is it important to know the number of trainable parameters in a neural network?

• To determine the model's accuracy

• To estimate the computational cost

• To calculate the model's speed

• To decide the number of layers

What is the number of learnable parameters in the input layer?

• 64

• 0

• 96

• 32

What is the kernel size used in the first convolutional layer?

• 4x4

• 5x5

• 2x2

• 3x3

What is the purpose of a dropout layer in a neural network?

• To increase the number of parameters

• To reduce overfitting by making an ensemble

• To enhance the learning rate

• To increase the input size

How many trainable parameters are there in the second convolutional layer?

• 2048

• 320

• 1024

• 9240

What is the effect of max pooling on the input image size?

• Reduces the size to half

• Keeps the size the same

• Triples the size

• Doubles the size

How many filters are used in the third convolutional layer?

• 64

• 32

• 128

• 16