Predictive Analytics with TensorFlow 8.3: Tuning CNN Hyperparameters

The video discusses the memory requirements of CNNs during training and inference, offering solutions for memory issues. It explains the use of max pooling layers versus convolutional layers and introduces the dropout technique to prevent overfitting. The RMS prop optimizer is also covered, detailing its function and parameters. The video concludes with a preview of a CNN-based predictive model for sentiment analysis.

What is one method to reduce memory usage when training a CNN?

• Reduce the mini-batch size

• Use 64-bit floats instead of 32-bit

• Increase the mini-batch size

• Add more layers to the network

Why might you choose a Max pooling layer over a convolutional layer with the same stride?

• Max pooling layers increase the number of features

• Max pooling layers require less computational power

• Max pooling layers have parameters that can be tuned

• Max pooling layers have no parameters

What is the purpose of local response normalization in CNNs?

• To increase the number of neurons in a layer

• To encourage feature maps to specialize and explore a wider range of features

• To reduce the number of layers in the network

• To increase the learning rate

What does the 'keep prob' parameter signify in the dropout method?

• The probability of removing a neuron

• The probability of keeping a neuron

• The learning rate of the network

• The number of neurons to be added

What is a suggested default value for the learning rate when using the RMSprop optimizer?

• 0.01

• 0.1

• 0.0001

• 0.001