AI and Machine Learning

Neural Network Optimization

Neural Network Optimization aims to enhance the performance, efficiency, and scalability of AI models for diverse applications. Core techniques include hyperparameter tuning, neural architecture search (NAS), pruning, quantization, and model compression, which reduce computational load and energy consumption. Optimized networks can be deployed on edge devices and resource-constrained environments. Research continues to balance accuracy, speed, and efficiency, enabling sustainable, cost-effective, and high-performing AI systems across industries.