👉 Valley engineering, also known as valley function or valley function optimization, is a critical process in deep learning that focuses on enhancing the performance of neural networks by optimizing their weights and biases within specific layers, particularly in the context of convolutional neural networks (CNNs). This technique involves adjusting the parameters within the "valley" regions of these layers, which are typically characterized by large gradients during backpropagation, indicating areas where small changes in weights can lead to significant improvements in model accuracy. By fine-tuning these regions, valley engineering helps mitigate issues like vanishing or exploding gradients, improves gradient flow, and ultimately leads to more efficient and effective training of deep neural networks. This optimization is often achieved through specialized algorithms like Layer-Wise Adaptive Learning Rates (LAMB) or Layer-wise Adaptive Rate Scaling (LARS), which dynamically adjust learning rates based on the valley characteristics of each layer.
