Neural Networks — Practice MCQs for CCAT

Correct Answer: B — Biological neurons in the brain

Artificial neural networks are inspired by the structure and function of biological neural networks in the brain.

Correct Answer: B — Single layer neural network with one neuron

A perceptron is the simplest neural network - a single neuron that makes binary classifications.

Correct Answer: B — Non-linearity to the network

Activation functions introduce non-linearity, allowing networks to learn complex patterns.

Correct Answer: B — f(x) = max(0, x)

ReLU (Rectified Linear Unit) returns x if positive, otherwise 0. Simple yet effective.

Correct Answer: B — 0 and 1

Sigmoid squashes values to range (0, 1), useful for probability outputs.

Correct Answer: B — Calculate gradients and update weights

Backpropagation calculates gradients of the loss with respect to weights, enabling weight updates.

Correct Answer: B — Loss/error function

Gradient descent iteratively adjusts weights to minimize the loss function.

Correct Answer: B — Controls step size in weight updates

Learning rate controls how much weights are adjusted in each update step during training.

Correct Answer: B — One complete pass through entire training dataset

An epoch is one complete pass through the entire training dataset during training.

Correct Answer: B — Neural networks with many layers

Deep learning uses neural networks with multiple hidden layers (deep networks) to learn hierarchical features.

Correct Answer: B — Gradients become very small in deep networks

In deep networks, gradients can become extremely small during backpropagation, preventing weight updates in early layers.

Correct Answer: B — Prevent overfitting by randomly dropping neurons

Dropout randomly deactivates neurons during training, preventing overfitting by reducing co-adaptation.

Correct Answer: B — Normalizes layer inputs for faster training

Batch normalization normalizes layer inputs, accelerating training and reducing internal covariate shift.

Correct Answer: B — Output layer for multi-class classification

Softmax converts raw outputs to probability distribution over multiple classes (summing to 1).

Correct Answer: B — Difference between predicted and actual values

Loss function quantifies how far model predictions are from actual target values.

Correct Answer: B — Momentum and adaptive learning rates

Adam combines momentum (from RMSprop) and adaptive learning rates for efficient optimization.

Correct Answer: B — Randomly with small values

Weights are randomly initialized with small values to break symmetry and enable learning.

Correct Answer: B — Are between input and output layers

Hidden layers are intermediate layers between input and output that learn internal representations.

Correct Answer: B — Classification tasks

Cross-entropy loss measures difference between predicted probability distribution and actual class labels.

Correct Answer: B — Using pre-trained model for new task

Transfer learning uses knowledge (weights) from a model trained on one task for a related task.