Photonic neural networks (PNNs) represent a promising intersection of optical computing and artificial intelligence, offering potential advancements in speed and energy efficiency. This paper explores the core architecture of PNNs, their underlying principles, and the methodologies for implementing software-defined neural networks onto photonic platforms. The study utilizes singular value decomposition (SVD) to convert neural network operations into unitary operations compatible with photonic components, such as Mach-Zehnder Interferometers (MZIs). Experimental results highlight the advantages of PNNs over traditional electronic neural networks, emphasizing their potential in real-world applications.