Thesis Abstract

Abstract
The field of artificial intelligence (AI) has seen significant advancements in recent years, with applications ranging from natural language processing to autonomous vehicles. One area of AI that has garnered attention is the use of machine learning algorithms for image recognition tasks. In this research project, we explore the effectiveness of various deep learning techniques for image classification. We conduct experiments using popular deep learning frameworks such as TensorFlow and PyTorch to compare the performance of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for image recognition. Our results show that CNNs outperform RNNs in terms of accuracy and computational efficiency for image classification tasks. Additionally, we investigate the impact of data augmentation techniques on improving the generalization capabilities of the models. Through our experiments, we demonstrate that data augmentation methods such as rotation, flipping, and scaling can enhance the performance of deep learning models in image classification tasks. Furthermore, we analyze the interpretability of the models by visualizing the learned features using techniques like activation maximization and gradient-weighted class activation mapping (Grad-CAM). By gaining insights into the inner workings of the deep learning models, we aim to provide a better understanding of how these models make decisions in image classification tasks. Overall, our research contributes to the growing body of knowledge in the field of artificial intelligence by providing insights into the performance, generalization, and interpretability of deep learning models for image classification. The findings from this study can have implications for various real-world applications such as medical image analysis, autonomous driving, and surveillance systems. Moving forward, we plan to explore more advanced architectures and techniques to further improve the performance and interpretability of deep learning models for image recognition tasks.

Thesis Overview