Introduction to Machine Coursera Week 3 Quiz
In this blog you will find the correct answer of the Coursera quiz Introduction to Machine Coursera Week 3 Quiz mixsaver always try to brings best blogs and best coupon codes
Week 3 Comprehensive
Which of the following indicates whether a doctor or machine is doing well at finding positive examples in a data set?
- Positive Predictive Value
- Likelihood Ratio
Which of the following is used to distinguish the false positive rate from the false negative rate?
- False Negative
- Negative Predictive Value
Which of the following is the best conceptual definition of one dimensional convolution?
- “Inverting” of a shape, where the inversion matches a feature.
- “Sliding” of two signals, where a matched feature gives a high value of convolution.
- “Intertwining” of two signals, where one wraps around the other to form a feature.
- “Distortion” of one signal, according to the feature shape
Which of the following can a user choose when designing a convolutional layer? (Choose all that are correct.)
- Filter depth
- Filter size
- Filter number
- Filter stride
- Filter weights
What is a fully connected readout?
- A layer with ten classifications.
- A layer with connections to all feature maps.
- The vectorization of a pooling layer.
- A layer with a single neuron for each output class.
Why are nonlinear activation functions preferable?
- Nonlinear activation functions are preferable because they are used in generalized linear models in statistics.
- Nonlinear activation functions increase the functional capacity of the neural network by allowing the representation of nonlinear relationships between features in input.
- Nonlinear activation functions are preferable because they have been used historically.
- Nonlinear activation functions are NOT preferable to linear ones, as they lose information in systems with high variance.
Which of the following are benefits of pooling? (Choose all that are correct.)
- Decreases bias.
- Combats overfitting.
- Vectorizes the data.
- Encourages translational invariance.
- Reduces computational complexity.
How are parameters that minimize the loss function found in practice?
- Fractal geometry
- Gradient descent
- Simplex algorithm
- Stochastic gradient descent
Which of the following is an advantage of hierarchical representation of image features?
- Eliminating bias.
- Decreasing the computational complexity.
- Better leveraging all training data.
- Decreasing variance in the model.
Why does transfer learning work?
- Top-level features are specialized for a particular task, while low-level features are universal to all images.
- All layers of filters can be learned by studying the mammalian receptive fields.
- Low-level features are specialized for a particular task, while top-level features are universal to all images.
- All images are composed of pixels with three color channels.