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@article{167392,
        author = {SHAIK UMAR FAROOQ and PANGULURI TEJAS RAJU and CHALLA SURYA SAI},
        title = {A Comprehensive Overview of Deep Learning Methods and Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1362-1367},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167392},
        abstract = {Deep learning, a branch of artificial intelligence focused on neural networks, has transformed numerous areas of study by offering unparalleled abilities in analyzing data and identifying patterns. Its method of learning, which structures complex concepts in data through various levels of nonlinear operations, has led to significant advancements in areas like computer vision, natural language understanding, and medical device design. This section delves into the basic concepts of deep learning, major design improvements, and notable applications in research. The progress of deep learning is highlighted by important milestones, such as the introduction of convolution neural networks (CNNs) for processing images, recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) for handling sequential data, and the emergence of generative adversarial networks (GANs) for creating new data. These structures have greatly enhanced the capabilities of current technology in areas like identifying objects in images, generating speech, and enabling self-driving cars. In this review article, explains the combination of deep learning with other technologies, like reinforcement learning and transfer learning, has broadened its scope and effectiveness. Reinforcement learning has allowed AI systems to surpass human capabilities in intricate games, while transfer learning has made it possible to adapt pre-trained models for specific tasks with little data.},
        keywords = {Convolution Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Generative Adversarial Networks (GANs)},
        month = {August},
        }