This chapter presents an in-depth exploration of classical and quantum neural network paradigms, encompassing the fundamental architectures, training methodologies, and theoretical analyses of network performance. This chapter is organized into five sections: Sect. 4.1 reviews the structural and theoretical foundations of classical neural networks; Sect. 4.2 introduces the quantum perceptron model, elucidating its theoretical advantages over classical counterparts; Sect. 4.3 explores quantum neural networks (QNNs), detailing the process by which classical data is encoded into quantum states and processed through parameterized quantum gates, thereby mitigating the challenges posed by large model sizes and high computational costs; Sect. 4.4 delves into the theoretical aspects of QNNs, emphasizing their expressivity, generalization, and trainability; and finally, Sect. 4.5 provides illustrative code implementations using benchmark datasets to demonstrate the practical viability of QNNs.