👉 Weight engineering is a critical aspect of deep learning model development, focusing on optimizing the architecture and parameters of neural networks to enhance their performance, efficiency, and generalization capabilities. It involves designing and adjusting the number of neurons, layers, and connections within a network to better fit the specific task at hand, such as classification or regression. Techniques like pruning, regularization, and layer normalization are employed to reduce model complexity, prevent overfitting, and improve computational efficiency. By carefully tuning these weights, engineers can create models that are not only more accurate but also faster and more resource-efficient, making them suitable for deployment in a variety of applications ranging from image recognition to natural language processing.
