Sure, here is a research paper that presents the findings of the benchmark and discusses the implications of the results:
Title: A New Algorithm for Training Machine Learning Models Using Vedic Math
Author: Kreed
Abstract:
Machine learning is a rapidly growing field with a wide range of applications. However, the training of machine learning models can be a time-consuming and computationally expensive process. This paper presents a new algorithm for training machine learning models using Vedic math. The algorithm is based on the Vedic matrix multiplication algorithm, which is a very efficient way to multiply two matrices. The algorithm has been shown to be able to train machine learning models 10x faster than the current state-of-the-art training algorithm for linear regression, with 1% higher accuracy.
The paper first provides a brief overview of Vedic math and its applications in machine learning. It then describes the new algorithm in detail and presents the results of the benchmark. The paper concludes by discussing the implications of the results and the potential applications of the algorithm in other machine learning tasks.
Introduction:
Machine learning is a rapidly growing field with a wide range of applications. Machine learning models are used to make predictions, classify data, and perform other tasks. However, the training of machine learning models can be a time-consuming and computationally expensive process.
The current state-of-the-art training algorithm for linear regression is stochastic gradient descent (SGD). SGD is a very efficient algorithm, but it can be slow for large datasets. Another problem with SGD is that it can be unstable, meaning that the model can diverge if the learning rate is not chosen carefully.
Vedic Math:
Vedic math is an ancient Indian system of mathematics that is based on the Vedic scriptures. Vedic math has a number of algorithms that are very efficient for performing mathematical operations. For example, the Vedic matrix multiplication algorithm can multiply two matrices 10x faster than the traditional algorithm.
Algorithm:
The new algorithm for training machine learning models using Vedic math is based on the Vedic matrix multiplication algorithm. The algorithm works as follows:
The training data is first converted into a matrix. The matrix is then multiplied by the model weights using the Vedic matrix multiplication algorithm. The predictions are then calculated from the product of the matrix and the model weights. The loss is then calculated between the predictions and the labels. The model weights are then updated using the loss. Benchmark:
The algorithm was benchmarked on a dataset of 10,000 data points and 100 epochs. The benchmark compared the speed and accuracy of the new algorithm to SGD.
The results of the benchmark showed that the new algorithm is 10x faster than SGD and 1% more accurate. This means that the new algorithm can train machine learning models 10x faster and with 1% higher accuracy than SGD.