Journal of Artificial Intelligence, Machine Learning and Soft Computing

ABSTRACT

Deep learning (DL) has emerged as a cornerstone of modern artificial intelligence (AI), enabling significant advancements in computer vision, natural language processing, speech recognition, and robotics. The effectiveness of deep learning systems largely depends on the architecture of neural networks and the optimization techniques employed during training. This paper provides a comprehensive review of key deep learning architectures, including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs). Furthermore, it explores optimization strategies, such as gradient-based methods, adaptive learning rates, regularization techniques, and neural architecture search (NAS), that improve model performance and convergence. Challenges related to computational complexity, overfitting, and training stability are discussed. The paper concludes with future directions, emphasizing lightweight architectures, energy-efficient optimization, and explainable deep learning models.

KEYWORDS: Deep Learning, Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Optimization Techniques, Neural Architecture Search