Introduction to Embedded Machine Learning Week 3 Quiz Answers

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Introduction to Embedded Machine Learning complete course is currently being offered by Edge Impulse through Coursera platform and is being taught by Shawn Hymel and Alexander Fred-Ojala.

SKILLS YOU WILL GAIN

– Arduino

– Machine Learning

– Embedded System Design

– Microcontroller

– Computer Programming

Also Check: How to Apply for Coursera Financial Aid

Introduction to Data Analytics for Business (Week 1 - 2) Quiz Answers - Coursera!

Audio Classification and Sampling Audio Signals Quiz Answers

Question 1) Which of the following are good uses of audio classification? Select all that apply.

  • Translating handwriting to computer-encoded text
  • Tracking wildlife
  • Identifying anomalous noises in a home or office
  • Translating spoken words to computer-encoded text

Question 2) In a digital system, audio signals are sampled at a regular interval. A sampling period of 125 microseconds translates to a sampling rate of what?

  • 44.1 kHz
  • 48 kHz
  • 8 kHz
  • 16 kHz

Question 3) A sampled audio signal with a bit depth of 8 bits has more resolution than an audio signal with a bit depth of 16 bits.

MFCCs and CNNs Quiz Answers

Question 1) You are creating a system to classify audio events whose maximum frequency is 10 kHz. Your sample rate needs to be at least what?

Question 2) Why do we use the Mel-frequency cepstral coefficients (MFCCs) as features for audio data?

  • They provide data augmentation to help create a more robust audio classifier.
  • They mimic how the human ear perceives sound.
  • They mimic how the human vocal cords and mouth produce sound.
  • They help prevent overfitting.

Question 3) What does a single node in a convolution layer of a convolutional neural network do?

  • It prevents a random selection of outputs of the previous layer from reaching the next layer.
  • It combines the outputs of the previous layer such that they each have a value between 0 and 1 and all sum to 1.
  • It filters the image using a kernel.
  • It slides a window over the image, selecting the highest pixel value in that window.

Question 4) Dropout layers are used to reduce underfitting.

Question 5) You plot the training loss over the entire training period, and you discover the graph appears as follows:

What is the most likely issue for not having a convergent (or near-maximally low) training loss?

  • Not enough training time
  • Overfit model
  • Underfit model
  • Too much training time

Implementation Strategies Quiz Answers

Question 1) You deploy a trained model to your embedded system. The model gives us 4 outputs, each corresponding to the probability of each class. How do you identify the predicted class?

  • Choose the class with probability over a threshold
  • Sum the probabilities together and divide by 4
  • Choose the class with the lowest probability score
  • Choose the class with the highest probability score

Question 2) You wish to choose a threshold for your classification system, so you create 2 histograms on the same plot: one for the output probabilities of your target class when the input is your target class and another when the input class is not the target class. You get the following:

Which threshold should you choose if you want to minimize false negatives?

Question 3) You use the same validation dataset to evaluate 4 different models, which produce the ROC curves given below. Which model is likely to give you the best accuracy for your application?



Question 4) A neural network (the ones we’ve looked at so far in the course) is probabilistic.

Question 5) In sensor fusion, all of the sensors must be the same type.

Implementation Strategies Quiz Answers

Question 1) Audio classification can be used to detect anomalies in machinery.

Question 2) A sample rate of 20 kHz would require a sampling period of what? Please give your answer in microseconds rounded to the nearest whole number.

Question 3) Your audio classification system samples at a rate of 16 kHz. Your target sounds should be at most what frequency?

Question 4) The collection of slices of Mel-frequency cepstral coefficients (MFCCs) describe how the frequencies in audio data change over time.

Question 5) A convolutional neural network (CNN) uses a fully-connected neural network to perform classification before performing convolution.

Question 6) Convolution layers in a convolutional neural network (CNN) perform feature extraction.

Question 7) You plot the training loss over the entire training period, and you discover the graph appears as follows:

The model is not as accurate as you require (i.e. it is underfit to the data). Which of the following are likely to help you get a more accurate model? Select all that apply.

  • Increase model complexity
  • Gather more data
  • Train for longer
  • Early stopping

Question 8) You plot the training loss over the entire training period, and you discover the graph appears as follows:

What is the most likely issue for having a divergent training loss over time?

  • Overfit model
  • Underfit model
  • Learning rate is too low
  • Learning rate is too high

Question 9) You wish to choose a threshold for your classification system, so you create 2 histograms on the same plot: one for the output probabilities of your target class when the input is your target class and another when the input class is not the target class. You get the following:


Which threshold should you choose if you want to minimize both false positives and false negatives equally (as best as possible)?

Question 10) Sensor fusion is a form of machine learning.



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