Enhancing Performance of Multi-Layered Deep Neural Networks in Image Classification through Efficient Optimization Techniques

They demonstrated remarkable performance, and the same happened with deep neural networks, which consisted of multiple layers, allowing them to learn complex patterns and features directly from raw data. But, in many cases, training these deep nets is usually a time and resource-consuming process. Researchers have suggested several optimization techniques to improve the performance of multi-layered deep neural networks to overcome this challenge. These techniques aim to increase the learning rate of networks, reduce overfitting, and reach an optimal solution faster. Applying these efficient optimization approaches will further improve the performance of deep neural networks with large numbers of layers for image classification, resulting in a more general and accurate model suitable for real-world scenarios.