DEVELOPING A BASIC NEURAL NETWORK TO CLASSIFY IMAGES FROM THE MNIST DATASET

Abstrak

My thesis focuses on an important and currently popular topic: “Developing a basic neural network to classify images from the MNIST dataset”.
This topic is very important for current and future research. The main goal of this project is to create a basic neural network capable of efficiently classifying images from the MNIST dataset, a critical benchmark for image classification tasks. The MNIST dataset is widely known and widely used in computer vision and machine learning. Designing an accurate neural network to classify images from this dataset is a crucial milestone in the development of more advanced computer vision systems. By exploring the basic architecture of a neural network, this study aims to provide an overview of the basic principles of image classification tasks. The PhD thesis deals with the complex process of training a neural network using the MNIST dataset, evaluating its performance and fine-tuning its classification accuracy. Through this research, researchers and professionals gain a deeper understanding of the basic concepts and techniques involved in developing neural networks for image classification. In addition, the results of this study contribute to the wider body of knowledge in computer vision and machine learning, enabling further advances and applications in areas such as object recognition, pattern analysis and visual perception. Finally, the development of a basic neural network to classify images from the MNIST dataset is a crucial step towards building more advanced and sophisticated computer vision systems. This doctoral thesis lays the foundation for further research and innovation in the field of image classification, providing valuable knowledge and methods for further research.