👉 Bundle engineering is an advanced technique in neural network architecture design that involves the strategic combination of multiple, specialized subnetworks or modules to form a cohesive and optimized model. This process leverages the strengths of each individual component, such as convolutional layers for spatial feature extraction or recurrent layers for temporal dynamics, to create a more powerful and efficient model. By carefully engineering the interactions between these bundles—through mechanisms like attention, gating, or shared weights—researchers can enhance the model's ability to capture complex patterns and relationships in data, leading to improved performance across a wide range of tasks, from image recognition to natural language processing. This approach allows for greater flexibility and adaptability in model design, making it a key strategy in modern deep learning research.
