Neural Networks and Deep Learning 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 and Deep Learning flashcards as text
What is the activation function that outputs values strictly between 0 and 1, commonly used in binary classification output layers?
Answer: Sigmoid
The sigmoid function squashes any real-valued input to the (0, 1) range, making it useful for binary probability outputs.
Which layer type in a convolutional neural network applies a learned filter to detect local patterns in an image?
Answer: Convolutional layer
A convolutional layer slides learned filters over the input to produce feature maps capturing local spatial patterns like edges and textures.
What problem does the 'vanishing gradient' phenomenon cause in deep neural networks?
Answer: Gradients become very small in early layers, halting learning
Vanishing gradients occur when backpropagated error signals shrink exponentially through many layers, making early weights nearly untrainable.
What is the purpose of a dropout layer in a neural network?
Answer: To randomly deactivate neurons during training to reduce overfitting
Dropout randomly sets a fraction of neuron activations to zero during each training step, acting as an ensemble regularization technique.
Which optimizer adapts the learning rate for each parameter based on historical gradient information?
Answer: Adam
Adam (Adaptive Moment Estimation) maintains per-parameter adaptive learning rates using estimates of first and second moments of gradients.
What does 'backpropagation' compute in a neural network training cycle?
Answer: The gradient of the loss with respect to each network weight
Backpropagation applies the chain rule to efficiently compute the gradient of the loss with respect to every weight, enabling gradient-based updates.