Evaluate the impact of an AI application used in the real world. (case study) : Working with Flower Images: Case Study -

The video tutorial discusses the use of Resnet in deep learning, highlighting its various variants and improvements over time. It explains the architecture of Resnet, including the concept of skip connections and bottleneck blocks, and emphasizes the importance of customization for better model performance. The tutorial also covers the practical application of Resnet, suggesting the use of open-source code from GitHub for flexibility and up-to-date features.

What is one of the main reasons for using ResNet from a GitHub repository?

• To increase the complexity of the model

• To ensure the latest version is used

• To reduce the number of layers

• To avoid using open-source code

What is a benefit of using ResNet in competitions?

• It guarantees a win

• It provides opportunities for customization and improved accuracy

• It allows for the use of pre-built models

• It limits the number of layers to 10

What was a significant improvement introduced in ResNet in 2016?

• The ability to train networks with over 1000 layers

• The introduction of AlexNet

• The removal of skip connections

• The use of only two layers in the architecture

What is the purpose of a skip connection in ResNet?

• To increase the number of layers

• To allow the network to skip learning certain layers

• To decrease the network's accuracy

• To remove the need for batch normalization

What is a bottleneck residual block designed for?

• Single-layer networks

• Networks without skip connections

• Deeper networks

• Shallow networks

What was a key feature of the ResNet 1001 architecture?

• It was limited to 10 layers

• It removed all skip connections

• It allowed direct signal propagation across the network

• It used a single layer for all operations

What is a key advantage of the bottleneck architecture in ResNet?

• It reduces the network's accuracy

• It eliminates the need for skip connections

• It allows for more complex networks with fewer parameters

• It simplifies the network to a single layer

What role does batch normalization play in ResNet architecture?

• It normalizes the input data

• It helps in propagating signals across the network

• It removes the need for skip connections

• It increases the number of layers

Why might one choose to use a ResNet variant from a GitHub repository instead of Keras?

• To reduce the number of available layers

• To have more flexibility in choosing variants

• To ensure the model is less complex

• To avoid using open-source code

What flexibility does using ResNet from a GitHub repository provide?

• The ability to choose from various ResNet variants

• The ability to use outdated versions

• The ability to avoid using skip connections

• The ability to remove all layers