Intro to ML: Neural Networks Lecture 2 Part 1
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Assessment
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Josiah Wang
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Mathematics, Computers, Fun
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University
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42 plays
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Hard
Student preview
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6 questions
Show answers
1.
Multiple Choice
Mean squared error is a common loss function for which task?
Regression
Classification
None of these
Regression and Classification
Answer explanation
Mean squared error is the expected (squared) distance between the models predictions and the true values. It therefore is well suited to regression tasks where the label space is a continuous one. MSE assumes the data is normally distributed however, in binary classification tasks, the data is distributed according to a Bernoulli distribution.
2.
Multiple Choice
Is the following statement True or False? Multi-class and Multi-label classification are the same thing.
True
False
Answer explanation
Multiclass classification - classification task where each instance needs to be assigned to one of two or more classes.
Mulitlabel classification - assign each instance a set of target labels. Think of this as predicting a series of properties of an instance which are not mutually exclusive.
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
Answer explanation
Every neuron in a fully connected neural layer is connected to every input via a weight. This creates a vector of dimensions equal to the input layer dimensions for every neuron in the current layer. Resulting in a stack of these vectors as deep as the number of neurons in the current layer. For Z=XW the neighbouring dimensions need to match.
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
Answer explanation
The key with this computational graph is to apply the chain rule at each node. Please see the attached solution for more information.
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