Training and Optimization of Artificial Intelligence Systems

Abstract

To develop reliable Artificial Intelligence (AI) systems, it is vital to have training and optimization as the basic steps. This paper presents sophisticated approaches for training and optimizing AI models, which highlights the importance of these methodologies as well as the challenges involved. The article also includes a step-by-step example of how to train a convolutional neural network on MNIST using Python and TensorFlow. It is possible to experience all the complexity along with scientific methods that are needed in current AI research by examining this instance, which includes algorithmic details as well as model evaluation metrics. Furthermore, I present an innovative approach for improving model training efficiency and accuracy during optimization; thereby presenting new perspectives on the future direction of AI.