Deep Learning and Neural Networks Flashcards
6 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Deep Learning and Neural Networks flashcards as text
What is the primary role of an activation function in a neural network?
Answer: To introduce non-linearity into the model
Activation functions introduce non-linearity, allowing neural networks to learn complex patterns beyond simple linear relationships.
Which optimization algorithm adapts the learning rate for each parameter using estimates of first and second moments of the gradients?
Answer: Adam
Adam (Adaptive Moment Estimation) combines momentum and RMSProp by tracking both the first and second moments of gradients.
What does the term 'epoch' mean in the context of training a neural network?
Answer: One complete pass through the entire training dataset
An epoch is one full pass through all training examples, after which weights have been updated based on every sample.
Which technique randomly drops neurons during training to reduce overfitting in neural networks?
Answer: Dropout
Dropout randomly sets a fraction of neuron outputs to zero during each training step, forcing the network to learn redundant representations.
In a convolutional neural network (CNN), what does a pooling layer primarily do?
Answer: Reduces spatial dimensions of feature maps
Pooling layers downsample feature maps by summarizing regions, reducing computation and providing translation invariance.
What is the vanishing gradient problem in deep neural networks?
Answer: Gradients shrink exponentially as they propagate back through layers, slowing learning
Vanishing gradients occur when repeated multiplication of small derivatives causes gradients to approach zero in early layers, preventing effective weight updates.