The proliferation of digital content platforms has rendered personalized recommendation systems a foundational component of modern information retrieval. Classical Matrix Factorization (MF) methods, while computationally tractable, are fundamentally constrained by the linearity of the inner product operator, which prevents them from capturing the non-linear, higher-order dependencies characteristic of real-world user–item interaction spaces. This paper presents a complete end-to-end system embodying Neural Collaborative Filtering (NCF), wherein a Generalized Matrix Factorization (GMF) module and a Multi-Layer Perceptron (MLP) are fused to model both linear and non-linear latent factors simultaneously. Two fully isomorphic implementations are developed: a pedagogical NumPy-based version featuring hand-derived backpropagation, and an optimized PyTorch version leveraging Apple Silicon MPS acceleration. Empirical evaluation on the MovieLens dataset demonstrates that both implementations converge to equivalent final Binary Cross-Entropy losses (0.2257 and 0.2307, respectively), while the PyTorch variant achieves a 3.39× speedup in total training time (3,295 s versus 972 s over 20 epochs). Peak recommendation quality, measured by Hit Ratio at cutoff 10 (HR@10), reaches 0.615 for the PyTorch implementation. The system is deployed as a production-grade microservices architecture comprising a FastAPI gateway, a dedicated PyTorch inference server, a PostgreSQL persistence layer, and a Netflix-style frontend with TMDB poster integration. A hybrid cold-start module supplements the NCF core for new users and items. The findings validate the feasibility of bridging rigorous algorithmic pedagogy with industry-standard deployment practices.