This paper compares Julia reduction and hyperbolic reduction with the aim of finding equivalent binary forms with minimal coefficients. We demonstrate that hyperbolic reduction generally outperforms Julia reduction, particularly in the cases of sextics and decimics, though neither method guarantees achieving the minimal form. We further propose an additional shift and scaling to approximate the minimal form more closely. Finally, we introduce a machine learning framework to identify optimal transformations that minimize the heights of binary forms. This study provides new insights into the geometry and algebra of binary forms and highlights the potential of AI in advancing symbolic computation and reduction techniques. The findings, supported by extensive computational experiments, lay the groundwork for hybrid approaches that integrate traditional reduction methods with data-driven techniques.

Hello guys, we have learnt about Linear Regression model in my previous article. Today, in this article we will get to learn the basics of Logistic Regression and some tricks to find the relation between the variables. Do you know what type of variable is used in logistic regression… Don't worry, if you don't know then let me teach the variables: In simple linear regression the variables are one dependent and one independent, In multiple linear regression there are more than one independent variable. Understand one thing if your data is in continuous form then use only linear regression model, while on the other hand, if your data is in categorical form(e.g. In this model the data been code in binary form.