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