Analyzing the effectiveness of post-learning quantization for optimizing neural networks
Journal: H&ES Research
Year: 2025

Technologies are developing rapidly, including neural networks. After the advent of deep learning, the models became more complex and deeper every year, which led to a shortage of hardware. The article discusses modern methods for optimizing neural networks with an emphasis on post-learning quantization as the most practical approach for deploying models in conditions of limited computing resources. An overview of key methods, including pruning, quantization, and distillation of knowledge, is pr