BDA5.2.1 Basics of Neural Networks

This skill introduces the foundational building blocks of neural networks, including how they are structured, trained, and optimized. It covers feedforward networks, loss functions, activation functions, and the backpropagation algorithm.

• External: Knowledge of basic linear algebra and calculus
• Internal: None

• Describe the structure of a feedforward neural network.
• Explain the role of activation functions and compare common types (ReLU, sigmoid, tanh).
• Understand how neural networks learn through backpropagation and gradient descent.
• Identify common loss functions for classification and regression tasks.
• Outline the training loop and how it is implemented in modern frameworks.

Caution: All text is AI generated