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WorksheetsDeep Learning - Q1
Total questions: 10
Worksheet time: 5mins
What is a neural network composed of?
python
Layers, neurons, and weights
Activation function only
Bias terms only
None of the options
Which activation function is commonly used in hidden layers?
Sigmoid
ReLu
TanH
Softmax
A multi-layer linear model is just a neural network without an activation function.
True
False
In a multi-layer linear model without activation functions, the intermediate layers are redundant, thus, useless.
True
False
What is the purpose of the `loss.backward()` function?
To calculate loss
To perform backpropagation
To update weights
To initialize gradients
Choose the option that best describes what is happening in the code.
Data preprocessing
Model evaluation
Model training
Data augmentation
Choose the options that are true about using non-linear functions in neural networks.
Non-linear functions allow neural networks to model complex relationships.
Non-linear functions reduce the need for multiple layers in a network.
Without non-linear functions, a neural network behaves like a single-layer linear model.
Using non-linear functions guarantees higher accuracy in every problem.
4o
During training, you observe that the gradients are becoming extremely small. What might be the cause of this issue?
The model is too large.
This is due to vanishing gradients.
The optimizer is not updating weights properly.
The learning rate is too high.
What is the primary function of the activation function in a neural network?
To initialize weights
To add non-linearity to the model
To compute the gradients
To reduce overfitting
Which activation function is commonly used in the output layer for binary classification problems?
Sigmoid
Softmax
Tanh
ReLu
