Deep Learning CNN Convolutional Neural Networks with Python - Resnet

The video tutorial introduces Resnet, a type of residual network, and explains its architecture, including the use of residual blocks and identity functions. It highlights the benefits of Resnet, such as reducing training errors and handling vanishing gradients. The tutorial also covers advanced features like batch normalization and 1x1 convolutions. Challenges in training deep networks are discussed, with transfer learning presented as a solution. Transfer learning allows the use of pre-trained models to train deep networks even with limited data.

What is the primary building block of a ResNet architecture?

• Residual block

• VGG block

• Dense block

• Inception block

How does a ResNet block help in learning complex patterns?

• By using dropout layers

• By reducing the number of parameters

• By learning the identity function

• By using more layers

What is a common challenge when training very deep convolutional neural networks?

• Vanishing and exploding gradients

• Overfitting on small datasets

• Insufficient number of layers

• Lack of computational power

What additional feature is often included in a ResNet block to handle vanishing gradients?

• Skip connections

• Pooling layers

• Batch normalization

• Dropout

What is the purpose of using 1x1 convolutions in ResNet?

• To increase the depth of the network

• To reduce the number of parameters

• To make tensors compatible for addition

• To enhance feature extraction

Why is transfer learning particularly useful for training deep networks with limited data?

• It simplifies the network architecture

• It allows the use of pre-trained weights

• It reduces the need for data augmentation

• It increases the number of layers

Which of the following is a well-known deep architecture similar to ResNet?

• SqueezeNet

• LeNet

• VGG 16

• AlexNet