This chapter examines the key components of neural networks, including input, hidden, and output layers, as well as the role of weights and activation functions in the learning process. Particular emphasis is placed on backpropagation as a core training technique. These concepts are explored in a structured manner, illustrated through the example of identifying handwritten digits, demonstrating how neural networks process and learn from data. The goal is to provide a clear and comprehensive introduction to the principles and mechanics of neural networks.