Which can help to prevent exploding gradients?

• Standardization

• Batch normalization

• Lowering the learning rate

• Standardization

___________ can help keep ReLU units from dying.

• Backpropagation

• Lowering the learning rate

• Batch normalization

• Standardization

• Updating the weights

Check the standard components of neural network.

• A set of nodes, analogous to neurons, organized in layers.

• A set of weights representing the connections between each neural network layer and the layer beneath it. The layer beneath may be another neural network layer, or some other kind of layer.

• A set of biases, one for each node.

• An activation function that transforms the output of each node in a layer. Different layers may have different activation functions.

“Convolutional Neural Networks can perform various types of transformation (rotations or scaling) in an input”. Is the statement correct True or False?

• True

• False

In custom modelling, which cases are true?

• Easy to get started

• Requires ML expertise

• Data is accessible to provider

• Robust support, auto fine-tuning

• Expensive procedure

Compute the following from the confusion matrix2. Sensitivity

• 85.91

• 87.51

• 95.23

• 85.79

• 91.78

In Automated ML, which cases are true?

• Robust Enterprise support

• Full control over parameter tuning

• Unlimited use-cases

• Difficult to extend

• Complete customization facility

Which of the following option is true?

• Linear Regression errors values has to be normally distributed but in case of Logistic Regression it is not the case

• Logistic Regression errors values has to be normally distributed but in case of Linear Regression it is not the case

• Both Linear Regression and Logistic Regression error values have to be normally distributed

• Both Linear Regression and Logistic Regression error values have not to be normally distributed

Standardisation / Normalization of features is required before training a Logistic Regression.

• True

• False

An enormous data set with randomly sampled examples probably :

• does not contain redundant data

• tends not to carry much more predictive value than large batches

• has more predictive value than a medium size data set