Neural Network Fundamentals

Describe the structure of a single artificial neuron, including weights, bias, and activation function

Explain the role of common activation functions (e.g., Sigmoid, ReLU, Softmax)

Describe the architecture of a multi-layer feedforward neural network

Explain the process of forward propagation to generate a prediction

Explain the concept of backpropagation and its role in calculating gradients for learning

Build and compile a sequential model for classification using a high-level framework (e.g., Keras)

Train a neural network using a framework, monitoring loss and accuracy

Tune key hyperparameters (learning rate, epochs, batch size) to improve model performance