Neural Networks Flashcards
6 cards from real Artificial Intelligence practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Neural Networks flashcards as text
What is the role of an activation function in a neural network?
Answer: Introduce non-linearity into the model
Activation functions add non-linearity, allowing neural networks to learn complex patterns beyond linear mappings.
Which activation function outputs values between 0 and 1 and is commonly used in binary classification output layers?
Answer: Sigmoid
The sigmoid function maps any real number to (0,1), making it suitable for binary probability outputs.
What is backpropagation in neural network training?
Answer: Using the chain rule to compute gradients and update weights
Backpropagation computes gradients of the loss with respect to all weights by applying the chain rule backwards through the network.
What is a 'vanishing gradient' problem in deep neural networks?
Answer: Gradients shrink to near zero as they propagate back, slowing learning in early layers
Vanishing gradients occur when gradients become too small during backpropagation, preventing early layers from learning effectively.
Which layer type in a neural network is fully connected, meaning every input neuron links to every output neuron?
Answer: Dense (fully connected) layer
A dense layer connects every neuron in the previous layer to every neuron in the next layer.
What does 'dropout' do during neural network training?
Answer: Randomly deactivates neurons during each training step to reduce overfitting
Dropout randomly sets a fraction of neuron activations to zero during training, acting as a regularization technique.