Sequences, Time Series and Prediction Coursera Quiz Answers

All Weeks Sequences, Time Series and Prediction Coursera Quiz Answers

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

Enroll on Coursera

Sequences, Time Series and Prediction Coursera Quiz Answers

Week 1 Quiz Answers:

Sequences, Time Series and Prediction

Q1: What is an example of a Univariate time series?

  • Hour by hour weather  
  • Baseball scores
  • Fashion items
  • Hour by hour temperature

Q2: What is an example of a Multivariate time series?

  • Baseball scores
  • Hour by hour temperature
  • Hour by hour weather
  • Fashion items

Q3: What is imputed data?

  • A good prediction of future data
  • A bad prediction of future data
  • A projection of unknown (usually past or missing) data
  • Data that has been withheld for various reasons

Q4: A sound wave is a good example of time series data

  • False
  • True

Q5: What is Seasonality?

  • Data that is only available at certain times of the year
  • A regular change in shape of the data
  • Weather data
  • Data aligning to the 4 seasons of the calendar

Q 6: What is a trend?

  • An overall consistent flat direction for data
  • An overall consistent downward direction for data
  • An overall consistent upward direction for data
  • An overall direction for data regardless of direction

Q7: In the context of time series, what is noise?

  • Sound waves forming a time series
  • Data that doesn’t have a trend
  • Data that doesn’t have seasonality
  • Unpredictable changes in time series data

Q8: What is autocorrelation?

  • Data that follows a predictable shape, even if the scale is different
  • Data that doesn’t have noise
  • Data that automatically lines up in trends
  • Data that automatically lines up seasonally

Q9: What is a non-stationary time series?

  • One that has a constructive event forming trend and seasonality
  • One that has a disruptive event breaking trend and seasonality
  • One that is consistent across all seasons
  • One that moves seasonally

Week 2 Quiz Answers: Sequences, Time Series and Prediction

Q1: What is a windowed dataset?

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

Q2: 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

Q3: 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]))

Q4: What does MSE stand for?

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

Q5: What does MAE stand for?

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

Q6: 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]

Q7: If you want to inspect the learned parameters in a layer after training, what’s a good technique to use?

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

Q8: How do you set the learning rate of the SGD optimizer?

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

Q9: 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

Week 3 Quiz Answers:

Sequences, Time Series and Prediction

Q1: If X is the standard notation for the input to an RNN, what are the standard notations for the outputs?

  • Y
  • H
  • Y(hat) and H
  • H(hat) and Y

Q2: What is a sequence to vector if an RNN has 30 cells numbered 0 to 29

  • The Y(hat) for the first cell
  • The total Y(hat) for all cells
  • The Y(hat) for the last cell
  • The average Y(hat) for all 30 cells

Q3: What does a Lambda layer in a neural network do?

  • Changes the shape of the input or output data
  • There are no Lambda layers in a neural network
  • Pauses training without a callback
  • Allows you to execute arbitrary code while training

Q4: What does the axis parameter of tf.expand_dims do?

  • Defines the dimension index to remove when you expand the tensor
  • Defines the axis around which to expand the dimensions
  • Defines if the tensor is X or Y
  • Defines the dimension index at which you will expand the shape of the tensor

Q5: A new loss function was introduced in this module, named after a famous statistician. What is it called?

  • Hubble loss
  • Hawking loss
  • Huber loss
  • Hyatt loss

Q6: What’s the primary difference between a simple RNN and an LSTM

  • LSTMs have a single output, RNNs have multiple
  • LSTMs have multiple outputs, RNNs have a single one
  • In addition to the H output, RNNs have a cell state that runs across all cells
  • In addition to the H output, LSTMs have a cell state that runs across all cells

Q7: If you want to clear out all temporary variables that tensorflow might have from previous sessions, what code do you run?

  • tf.cache.clear_session()
  • tf.keras.backend.clear_session() 
  • tf.keras.clear_session
  • tf.cache.backend.clear_session()

Q8: What happens if you define a neural network with these two layers?

tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),

tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),

tf.keras.layers.Dense(1),

  • Your model will fail because you have the same number of cells in each LSTM
  • Your model will fail because you need return_sequences=True after the first LSTM layer
  • Your model will compile and run correctly
  • Your model will fail because you need return_sequences=True after each LSTM layer

Week 4 Quiz Answers:

Sequences, Time Series and Prediction

Q1: How do you add a 1 dimensional convolution to your model for predicting time series data?

  • Use a 1DConvolution layer type
  • Use a Conv1D layer type
  • Use a Convolution1D layer type
  • Use a 1DConv layer type

Q2: What’s the input shape for a univariate time series to a Conv1D?

  • []
  • [None, 1]
  • [1]
  • [1, None]

Q3: You used a sunspots dataset that was stored in CSV. What’s the name of the Python library used to read CSVs?

  • CommaSeparatedValues
  • PyFiles
  • CSV
  • PyCSV

Q4: If your CSV file has a header that you don’t want to read into your dataset, what do you execute before iterating through the file using a ‘reader’ object?

  • reader.next
  • reader.ignore_header()
  • reader.read(next)
  • next(reader)

Q5: When you read a row from a reader and want to cast column 2 to another data type, for example, a float, what’s the correct syntax?

  • float f = row[2].read()
  • You can’t. It needs to be read into a buffer and a new float instantiated from the buffer
  • Convert.toFloat(row[2])
  • float(row[2])

Q6: What was the sunspot seasonality?

  • 11 years
  • 11 or 22 years depending on who you ask
  • 4 times a year
  • 22 years

Q7: After studying this course, what neural network type do you think is best for predicting time series like our sunspots dataset?

  • RNN / LSTM
  • DNN
  • Convolutions
  • A combination of all of the above

Q8: Why is MAE a good analytic for measuring accuracy of predictions for time series?

  • It punishes larger errors
  • It biases towards small errors
  • It only counts positive errors
  • It doesn’t heavily punish larger errors like square errors do
Sequences, Time Series and Prediction Coursera Course Review:

In our experience, we suggest you enroll in this Sequences, Time Series and Prediction Course and gain some new skills from Professionals completely free and we assure you will be worth it.

Sequences, Time Series and Prediction course is available on Coursera for free, if you are stuck anywhere between quiz or graded assessment quiz, just visit Networking Funda to get Sequences, Time Series and Prediction Coursera Quiz Answers

Conclusion:

I hope this Sequences, Time Series and Prediction Coursera Quiz Answers would be useful for you to learn something new from this Course. If it helped you then don’t forget to bookmark our site for more Coursera Quiz Answers.

This course is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisite courses.

Keep Learning!

<< Previous Course Quiz Answers

Natural Language Processing in TensorFlow

All Course Quiz Answers of DeepLearning.AI TensorFlow Developer Professional Certificate

Course 01: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Course 02: Convolutional Neural Networks in TensorFlow

Course 03: Natural Language Processing in TensorFlow

Course 04: Sequences, Time Series, and Prediction

Leave a Reply

error: Content is protected !!