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@article{206443,
        author = {Nethani Shivakumar and Shaik Meer Subhani Ali and CH. Tharun Kumar and Manikantha Desu},
        title = {A Review of Deep Learning Techniques: Basic Algorithms and Use Cases},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {1581-1588},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206443},
        abstract = {Deep Learning (DL), a specialized branch of artificial intelligence (AI) and machine learning (ML), has revolutionized modern computing by enabling systems to learn hierarchical representations from large volumes of data. Unlike conventional machine learning algorithms that require manual feature engineering, deep learning models automatically extract meaningful features through multiple hidden layers of artificial neural networks. Recent advances in computational power, graphics processing units (GPUs), cloud computing, and the availability of large-scale datasets have accelerated the adoption of deep learning across diverse application domains. This review paper provides a comprehensive overview of fundamental deep learning concepts, basic algorithms, and their real-world applications. The paper discusses the architecture, working principles, strengths, limitations, and practical implementations of major deep learning algorithms, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Autoencoders, Generative Adversarial Networks (GANs), and Transformer-based models. Furthermore, various use cases in healthcare, agriculture, finance, cybersecurity, transportation, natural language processing, robotics, and smart cities are explored. The paper also highlights current research challenges such as data scarcity, computational complexity, explainability, bias, privacy, and ethical concerns. Finally, future research directions involving Explainable AI, Federated Learning, Edge AI, TinyML, Graph Neural Networks, multimodal learning, and Generative AI are discussed. This review serves as a valuable reference for researchers, academicians, and practitioners interested in understanding the current state and future prospects of deep learning technologies.},
        keywords = {Deep Learning, Artificial Intelligence, Neural Networks, CNN, RNN, LSTM, Transformers, Computer Vision, Natural Language Processing, Machine Learning.},
        month = {July},
        }