32 Questions
What is the function of a neural network?
Represent a series of computational steps
What is the role of the nodes in a neural network?
Serve as neurons and perform computations
What is the purpose of the activation function in a neural network?
Map the sum of products to a scalar output value
In a feedforward network, where is the prediction (probability) located?
In the output layer
What do the nodes in the output layer represent in a multiclass problem?
Model's prediction for each possible class of inputs
What defines the architecture of a neural network?
The number of hidden layers and the number of nodes in each hidden layer
How is a neural network described in terms of its structure?
A feedforward network with input, hidden, and output layers
In a fully connected neural network, what does each unit (node) provide input to?
Each unit in the previous layer
How are the weights calculated from the input layer to the hidden layer if the input has 3 features and the hidden layer has 4 nodes?
$3 \times 4$
What determines whether or not a model will learn anything in a neural network?
The number of parameters to learn
What are some rules of thumb for selecting the proper neural network architecture?
The number of nodes in the first hidden layer should match or exceed the number of input vector features
What should be done if a model learns with one hidden layer in a neural network?
Add a second hidden layer to see if that improves things
What is the curse of dimensionality in relation to neural networks?
It leads to an increase in the amount of training data needed
In a feedforward neural network, where do loops occur?
There are no loops in a feedforward network
How are the weights calculated from Hidden Layer 1 to Hidden Layer 2 if Hidden Layer 1 has 4 nodes and Hidden Layer 2 has 4 nodes as well?
$4 \times 4$
How many biases are there when going from Hidden Layer 2 to Output Layer if there are 4 nodes in Hidden Layer 2 and only 1 node in Output Layer?
5
What is the purpose of the activation function in a neural network?
To produce a single scalar output value
In a feedforward network, where is the prediction (probability) located?
In the output layer
How are the weights calculated from the input layer to the hidden layer if the input has 3 features and the hidden layer has 4 nodes?
By multiplying each feature by a weight value for each node in the hidden layer
What is the role of the nodes in a neural network?
To accept inputs, multiply by weights, sum these products, and pass to an activation function
What are some rules of thumb for selecting the proper neural network architecture?
Creating a balance between model complexity and overfitting
What do the nodes in the output layer represent in a multiclass problem?
The prediction for each of the possible classes of inputs
What is the function of the hidden layers in a neural network?
To accept input from the previous layer and pass it to the next layer
How are the weights calculated from the input layer to the hidden layer if the input has 3 features and the hidden layer has 4 nodes?
3x4 = 12
What is a 'fully connected' neural network?
A network where each unit provides input to all units in the next layer
How many parameters are there to learn in a neural network with multiple layers and nodes?
Equal to the number of weights and biases combined
What determines whether or not a model will learn anything in a neural network?
The amount of training data needed
In a feedforward neural network, where do loops occur?
There are no loops in a feedforward neural network
What should be done if a model learns with one hidden layer in a neural network?
Add more hidden layers
In a fully connected neural network, what does each unit (node) provide input to?
All units in the next layer
What is the role of the nodes in a neural network?
To transform inputs into outputs using weights and biases
What do some rules of thumb suggest for selecting the proper neural network architecture?
Match or exceed the number of input vector features with nodes in the first hidden layer
This quiz covers the fundamental concepts of neural networks, focusing on their anatomy and structure. It discusses the graph-like representation, nodes, edges, and the universal function approximation ability of neural networks.
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