<![CDATA[Deep learning has revolutionized the field of artificial intelligence by enabling significant advancements across various domains, including computer vision, natural language processing, and speech recognition. This survey provides a comprehensive overview of recent developments in deep learning, focusing on three core aspects: architectural innovations, optimization strategies, and real-world applications. We explore the evolution of neural network architectures, from classical feedforward networks to cutting-edge models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs). In addition, we examine state-of-the-art optimization techniques, including adaptive learning rate methods, regularization strategies, and training heuristics that address challenges like vanishing gradients and overfitting. Finally, we present a broad spectrum of deep learning applications, highlighting breakthroughs in autonomous systems, healthcare, finance, and more. By synthesizing recent research trends and identifying emerging challenges, this survey aims to serve as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of deep learning.]]>
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