👉 Elimination computing is an optimization technique used in machine learning and deep learning where the goal is to minimize the number of operations (or "computations") performed during the training process. Instead of computing all possible intermediate activations and gradients, elimination computing approximates these values using a series of linear transformations and projections, effectively "eliminating" unnecessary intermediate steps. This approach significantly reduces computational complexity and memory usage, making it particularly useful for large neural networks where traditional backpropagation can be computationally expensive and memory-intensive. By focusing on the most critical components of the network, elimination computing aims to achieve similar or even better performance with fewer resources.
