Efficient Deep

Efficient deep learning focuses on developing neural network models and training algorithms that minimize computational resources while maintaining high accuracy. Current research emphasizes techniques like model compression (e.g., pruning, quantization, low-rank approximations), optimized architectures (e.g., EfficientNet, depthwise separable convolutions), and improved training methods (e.g., sparse backpropagation, adaptive sampling). These advancements are crucial for deploying deep learning on resource-constrained devices (e.g., mobile phones, embedded systems) and for reducing the environmental impact of large-scale training.