WorksheetsNeural Networks Quiz
Total questions: 10
Worksheet time: 5mins
What does the term 'hyperparameters' refer to in neural network training?
Parameters that are learned during training
The input values provided to the network
Settings like the number of layers and nodes, which are set before training
Super parameters that provide the best performance
NN weights are hyperparameters as they are the most important in NNs
Why might one choose to use a neural network instead of linear regression for a given dataset?
Neural networks are always faster to train
Neural networks can model complex, non-linear relationships better than linear regression
Neural networks require less data
Linear regression cannot be used for regression tasks
In the context of neural networks, what does 'convergence' refer to?
The point where the weights are randomly set
The moment when the learning rate is maximized
When the predicted output matches the actual output closely enough, stopping further updates
When the number of hidden layers is increased
Which of the following is NOT a typical application of deep learning?
Self-Driving Cars
News Aggregation
Visual Recognition
Simple Arithmetic Calculations
Which of the following best describes the Gradient Descent algorithm?
A method to increase the error between actual and predicted values
An iterative process to update weights to minimize the error
A technique to initialize weights randomly
A process to normalize input data
Given a non-linear regression problem , how can we use a neural network to solve it ?
by adding more data
use hyperparameters to reduce overfitting
increase number of Hidden nodes
increase number of layers
Use a sigmoid function to provide non-linearity
What is the primary function of a neuron in an artificial neural network?
To store data
To receive input and compute an output based on weighted sums
To transmit data between layers
To execute conditional statements
In a single-layer perceptron, what is the role of the bias term (b)?
It multiplies the input directly
It acts as a threshold for activation
It adds a constant value to the weighted sum
It adjusts the learning rate
What key concept allows deep learning models to perform tasks like language translation and image recognition effectively?
Single-layer perceptrons
Hand-coded feature extraction
Multiple layers (depth) in the network
High learning rates
What is the primary difference between Stochastic Gradient Descent (SGD) and Batch Gradient Descent (BGD)?
SGD updates weights after each instance, while BGD updates weights after all instances in the dataset
SGD uses multiple layers, whereas BGD uses a single layer
SGD is used for regression tasks, while BGD is used for classification tasks
There is no significant difference between SGD and BGD
