Quiz.md

1. What is a windowed dataset?

   • The time series aligned to a fixed shape
   • A consistent set of subsets of a time series
   • There’s no such thing
   • A fixed-size subset of a time series

2. What does ‘drop_remainder=true’ do?

   • It ensures that the data is all the same shape
   • It ensures that all data is used
   • It ensures that all rows in the data window are the same length by cropping data
   • It ensures that all rows in the data window are the same length by adding data

3. What’s the correct line of code to split an n column window into n-1 columns for features and 1 column for a label?

   • dataset = dataset.map(lambda window: (window[n-1], window[1]))
   • dataset = dataset.map(lambda window: (window[:-1], window[-1:]))
   • dataset = dataset.map(lambda window: (window[-1:], window[:-1]))
   • dataset = dataset.map(lambda window: (window[n], window[1]))

4. What does MSE stand for?

   • Mean Second error
   • Mean Squared error
   • Mean Slight error
   • Mean Series error

5. What does MAE stand for?

   • Mean Average Error
   • Mean Advanced Error
   • Mean Absolute Error
   • Mean Active Error

6. If time values are in time[], series values are in series[] and we want to split the series into training and validation at time 1000, what is the correct code?

   • time_train = time[split_time]
     x_train = series[split_time]
     time_valid = time[split_time]
     x_valid = series[split_time]
   • time_train = time[split_time]
     x_train = series[split_time]
     time_valid = time[split_time:]
     x_valid = series[split_time:]
   • time_train = time[:split_time]
     x_train = series[:split_time]
     time_valid = time[split_time]
     x_valid = series[split_time]
   • time_train = time[:split_time]
     x_train = series[:split_time]
     time_valid = time[split_time:]
     x_valid = series[split_time:]

7. If you want to inspect the learned parameters in a layer after training, what’s a good technique to use?

   • Assign a variable to the layer and add it to the model using that variable. Inspect its properties after training
   • Run the model with unit data and inspect the output for that layer
   • Iterate through the layers dataset of the model to find the layer you want
   • Decompile the model and inspect the parameter set for that layer

8. How do you set the learning rate of the SGD optimizer?

   • Use the Rate property
   • Use the RateOfLearning property
   • You can’t set it
   • Use the lr property

9. If you want to amend the learning rate of the optimizer on the fly, after each epoch, what do you do?

   • Use a LearningRateScheduler and pass it as a parameter to a callback
   • Callback to a custom function and change the SGD property
   • Use a LearningRateScheduler object in the callbacks namespace and assign that to the callback
   • You can’t set it