Learning representations by forward-propagating errors
Ryoungwoo Jang
Back-propagation (BP) is widely used learning algorithm for neural network
optimization. However, BP requires enormous computation cost and is too slow to
train in central processing unit (CPU). Therefore current neural network
optimizaiton is performed in graphical processing unit (GPU) with compute
unified device architecture (CUDA) programming. In this paper, we propose a
light, fast learning algorithm on CPU that is fast as CUDA acceleration on GPU.
This algorithm is based on forward-propagating method, using concept of dual
number in algebraic geometry.