Python for Deep Learning - Build Neural Networks in Python - Fully Connected Layer

The video tutorial explains the process of feature extraction in Convolutional Neural Networks (CNNs) using convolution and pooling layers. It describes how these layers detect features like edges and facial features in images. The tutorial then covers the integration of fully connected layers, which are similar to those in Artificial Neural Networks (ANNs), and explains the flattening process to convert 2D outputs into 1D vectors for classification. Finally, it provides an overview of the complete CNN process and transitions to the next lecture.

What is the primary function of convolution and pooling layers in a CNN?

• To classify images into categories

• To extract features from images

• To convert 1D vectors into 2D matrices

• To generate random noise in images

How do convolution and pooling layers contribute to the CNN architecture?

• By classifying images directly

• By extracting and summarizing features from images

• By converting images into grayscale

• By increasing the size of the input image

Why is it necessary to flatten the output of pooling layers before feeding it into fully connected layers?

• Because fully connected layers require a 1D vector input

• To increase the dimensionality of the data

• To reduce the computational cost

• To enhance the image resolution

What happens to the 2D output of the final pooling layer in a CNN?

• It is discarded

• It is converted into a 1D vector

• It is used as the final output

• It is transformed into a 3D matrix

What is the role of fully connected layers in a CNN?

• To connect extracted features to the output layer for classification

• To pool features from different layers

• To extract features from images

• To perform data augmentation