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WorksheetsQuiz-2
Total questions: 12
Worksheet time: 4mins
How does a Perceptron differ from a Neuron in ANN
Perceptrons and Neurons are identical terms
Neurons are simpler computational units compared to Perceptrons
Perceptrons are biological, while Neurons are artificial
There is no difference; both terms can be used interchangeably
In a Perceptron, what are weights and biases used for?
To increase the computational complexity of the model
To adjust the learning rate during training
To control the activation function of the perceptron
To modulate the strength of input signals and introduce an offset
What is the role of an activation function in a neural network?
To initialize the weights of the network
To control the learning rate during training
To introduce non-linearity into the model
To adjust the bias terms in each layer
How does the Vanishing Gradient Problem affect the training of deep neural networks?
It accelerates the convergence of the model
It leads to faster training times
It makes training slow and difficult for deeper layers
It has no impact on the training process
What is the role of the input layer in a neural network?
To make predictions
To process incoming data and pass it to the output layer
To adjust weights during training
To provide feedback during backpropagation
What is the primary use of CNNs in machine learning?
Natural Language Processing
Image and Video Recognition
Time Series Forecasting
Reinforcement Learning
What is the role of pooling layers in a CNN?
To flatten the input data
To introduce non-linearity
To calculate gradients during backpropagation
To reduce the spatial dimensions of the input data
During Forward Propagation, what is passed through the neural network from the input layer to the output layer?
Gradients
Weights and biases
Input data
Activation functions
How does the visualization of the Sigmoid activation function typically appear?
As a straight line
As an inverted "U" shape
As a step function
As an exponential curve
If a model has an accuracy of 90%, what does this imply?
The model is highly accurate and performs well in all scenarios
The model is making incorrect predictions 10% of the time
The model is likely overfitting the training data
The model is underfitting the training data
Why are hidden layers called "hidden" in a neural network?
They are invisible to the human eye
They contain confidential information
They are not directly connected to the input or output
They are used for debugging purposes
What is the role of optimizers in the context of training a machine learning model?
To initialize model weights
To preprocess input data
To adjust learning rates
To optimize the model's performance during training
