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University
Intro to ML: Neural Networks Lecture 2 Part 1
6 questions
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- 1. Multiple Choice
Mean squared error is a common loss function for which task?
Regression
Classification
None of these
Regression and Classification
- 2. Multiple Choice
Is the following statement True or False? Multi-class and Multi-label classification are the same thing.
True
False
- 3. Multiple Choice
The shape of the weight matrix, W of a Neural network linear layer is (x,y). A forward pass through this layer can be represented as follows:
Z=XW
Where X is the batched data of dimensions (batch size, input features) and W is the weight matrix. Select the correct assignments for (x, y)
x = batch size, y = number input features to that layer
x = number input features to that layer, y =batch size
x = input features, y = number of neurons in that layer
x = batch size, y = number of neurons in that layer
none of the above
- 4. Multiple Choice
If a neural network has a single output neuron, then the model may be used for:
Binary classification
Regression
Binary classification or regression
None of these
- 5. Multiple Choice
Here we have a computational graph representing a series of operations. The green text (text above the line) represents the forward pass (i.e. the values at each stage in the graph during forward propagation). The red values (the values which are positioned underneath the lines) represent gradient signals which have been passed back down the computational graph after some loss as been calculated. Calculate the missing gradients a, b and c using backpropagation (slides 20-31 onwards).
a= -0.2, b= 0.2, c=0.4
a= 0.2, b= -0.2, c=0.4
a= 0.4, b= 0.4, c=0.2
a= -0.2, b= -0.2, c=0.2
- 6. Multiple Choice
Which of the following are correct statements about the models which created the plots i) and ii)?
The model which created plot i) is demonstrating high bias and a higher capacity model is needed
The model which created plot ii) is demonstrating high bias and a higher capacity model is needed
The model which created plot ii) is demonstrating high variance and requires some form of regularisation to avoid over fitting.
The model which created plot i) is demonstrating high variance and requires some form of regularisation to avoid over fitting.
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