Problem Set 2 1. Model Parameters versus Hyperparameters

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Problem Set 2
1. Model Parameters versus Hyperparameters [3 points]:
(a) In your own words, describe what is a model parameter and hyperparameter and how they differ.
2. Dataset Splits [4 points]:
(a) In your own words, describe the motivation for creating a validation
dataset and its purpose for developing deep learning models.
(b) In your own words, describe what it means to create a stratified split
when dividing your dataset into training, validation, and test splits and
what is the motivation.
3. Overfitting versus Underfitting [2 points]:
(a) If your model performs poorly on the training data and poorly on new
examples, is the model overfitting or underfitting?
(b) If your model performs well on the training data and generalizes poorly
to new examples, is the model overfitting or underfitting?
4. Model Size [6 points]: Assume you are building a 5 layer neural network
that takes as input a 32×32 matrix of integers, has 10 nodes per hidden layer,
and has 100 nodes at the output layer.
(a) If this network was fully connected, how many model parameters would
it have? For full credit, you must indicate the number of model parameters between each pair of layers (e.g., input to hidden layer 1), the
total number of weights in the network, and the total number of bias
terms in the network.
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(b) If this network had convolutional layers with 5 3×3 filters per layer for
all hidden layers, how many model parameters would it have? For full
credit, you must indicate the number of model parameters between each
pair of layers (e.g., input to hidden layer 1), the total number of weights
in the network, and the total number of bias terms in the network.
5. Convolutional Neural Networks [10 points]:
(a) In your own words, describe at least two advantages of using convolutional layers instead of fully connected layers in a neural network.
(b) In your own words, describe at least two advantages of using pooling
layers instead of fully connected layers in a neural network.
(c) Compute and report the output that you would get from applying the
filter on the input data, shown below, when using ‘SAME padding’.
In order to get partial credit for the case that the output is incorrect,
(d) Compute and report the output that you would get from applying the
filter on the input data, shown below, when using 2×2 stride. In order
to get partial credit for the case that the output is incorrect, include
1 1 0 2
4 0 8 10
6 4 2 0
8 7 4 2
10 7 5 1
Data type 1: Input
0 0.5 0
0.5 1 0.5
0 0.5 0
Data type 2: Filter
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