Neural Networks MCQ · test your knowledge
From perceptron to backpropagation – 15 questions covering architecture, activation, optimisation & regularisation.
Neural networks: essential building blocks
Artificial neural networks are computing systems vaguely inspired by biological brains. They consist of interconnected units (neurons) that process information using connectionist approaches. This MCQ test covers the foundational elements every deep learning practitioner must know.
What is a neural network?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. At its core, it comprises layers of neurons: input, hidden, and output.
Core concepts tested
Perceptron
The simplest form of a neural network, used for binary classification. It computes a weighted sum of inputs and applies a step function. The perceptron convergence theorem is fundamental.
Activation functions
Introduce non‑linearity: Sigmoid (0,1), Tanh (-1,1), ReLU max(0,x). Without them, stacked layers would be equivalent to a single linear transform.
Backpropagation
Algorithm to train neural networks using the chain rule. It computes gradients of the loss w.r.t each weight, then updates via gradient descent.
Weight initialisation
Proper initialisation (Xavier, He) prevents vanishing/exploding gradients. Bad init can stall training.
Regularisation
Dropout, L1/L2, early stopping – techniques to reduce overfitting in overparameterised networks.
Loss functions
MSE for regression, cross‑entropy for classification. The choice depends on the task and output layer activation.
# Simple perceptron update (Python pseudo)
if prediction != target:
for i in range(n_inputs):
weights[i] += learning_rate * target * inputs[i]
Common interview questions
- What is the difference between a perceptron and a logistic regression model?
- Why is ReLU non‑linear and why is it preferred over sigmoid?
- How does backpropagation use the chain rule?
- What is the role of the bias term in a neuron?
- Explain the vanishing gradient problem and solutions.