  Deep learning has revolutionized industries like computer vision, natural
language processing, and speech recognition. However, back propagation, the
main method for training deep neural networks, faces challenges like
computational overhead and vanishing gradients. In this paper, we propose a
novel instant parameter update methodology that eliminates the need for
computing gradients at each layer. Our approach accelerates learning, avoids
the vanishing gradient problem, and outperforms state-of-the-art methods on
benchmark data sets. This research presents a promising direction for efficient
and effective deep neural network training.
