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The problem of load estimation from sensor signals holds significance in the field of intelligent manufacturing. The goal of this work is to estimate the axial and spindle load values in a Computer Numerical Control machine from input sensor readings like spindle speed, feed rate, tool positions, etc. This can be viewed as a standard regression problem. Here, we propose a novel deep learning based regression technique that incorporates regression within the stacked autoencoder framework.