A Machine Learning Model for Training Your AI

Akaninyene Udoeyop1

1

Publication Date: 2024/09/03

Abstract: Artificial Intelligence is playing an increasing role in solving some of the world’s biggest problems. Machine Learning Models, within the context of reinforcement learning, define and structure a problem in a format that can be used to learn about an environment in order to find an optimal solution. This includes the states, actions, rewards, and other elements in a learning environment. This also includes the logic and policies that guide learning agents to an optimal or nearly optimal solution to the problem. This paper outlines a process for developing machine learning models. The process is extensible and can be applied to solve various problems. This includes a process for implementing data models using multi-dimensional arrays for efficient data processing. We include an evaluation of learning policies, assessing their performance relative to manual and automated approaches.

Keywords: No Keywords Available

DOI: https://doi.org/10.38124/ijisrt/IJISRT24JUL769

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JUL769.pdf

REFERENCES

  1. Manasi Vartak , Harihar Subramanyam , Wei-En Lee , Srinidhi Viswanathan , Saadiyah Husnoo , Samuel Madden , Matei Zaharia, 2016. ModelDB: A System for Machine Learning Model Management.
  2. Emily Sullivan, 2022. Understanding from Machine Learning Models: The British Journal for the Philosophy of Science, Volume 73, Number 1.
  3. James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viegas, and Jimbo Wilson, 2020. The What-If Tool: Interactive Probing of Machine Learning Models: IEEE Transactions On Visualization And Computer Graphics, Vol. 26, No. 1.
  4. Christopher M. Bishop, 2013, Model-Based Machine Learning: Phil Trans R, Soc A 371: 20120222