👉 Layer engineering in neural networks refers to the systematic design and optimization of the various layers within a neural network architecture to enhance its performance, efficiency, and ability to learn complex patterns. This process involves carefully selecting the number of layers, the type of layers (e.g., convolutional, recurrent, fully connected), and the specific parameters within each layer, such as activation functions, filter sizes, and connection weights. By strategically engineering these layers, engineers can tailor the network to specific tasks, balancing factors like model complexity, computational cost, and accuracy. For instance, in image recognition, convolutional layers might be used to extract spatial features, while fully connected layers handle the classification task. Layer engineering is crucial for developing state-of-the-art models that can effectively process and interpret large-scale data.
