Accelerating Gradient Slope Computation with K1 Fluu: A Novel Technique for Neural Network Training
Naina Chaudhary, Astha Gupta, Gurinder Singh, Ayush Thakur, Pratap Paraji Patil, Vinita Sharma
Abstract
This report is all about the invention called the K1 Fluu, a new method in fast learning neural networks. K1 Fluu gets gradient slopes easier and yields hidden weights higher. It applies higher order derivatives, weight change, and a unique smoothing technique to improve the speed of the gradient slope. We demonstrate many difficult math operations that describe the essence of the functioning of K1 Fluu. I hope these equations make K1 Fluu understand how the partial derivatives to the parameters of the shapes of the loss function. We compared K1 Fluu on standard benchmarks and state-of-art neural networks. The testing confirms that K1 Fluu is more effective than other procedures. This makes the neural networks more precise, acquire knowledge within a faster time and also have a short training time. The performer K1 Fluu has the advantages in that it can change and smoothes things in order to make the learning process more stable and efficient. This report provides the field of neural network training with a powerful new tool. It enhances network performance and creates the need for other areas to be researched.