Deep Learning Models and Neural Network Optimization Strategies: A Review

Akash Dattatray Raut

Abstract: Deep learning is one of the fastest-growing technologies in artificial intelligence (AI). It enables machines to learn from large datasets and perform tasks such as image recognition, speech processing, language translation, healthcare prediction, and autonomous decision-making. The success of deep learning depends on both neural network architectures and optimization techniques that improve model performance and training efficiency. This review paper discusses major deep learning paradigms including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The paper also explains important optimization techniques such as backpropagation, gradient descent, dropout, batch normalization, and early stopping. Advanced architectures like Convolutional Neural Networks (CNNs), ResNet, and transfer learning are reviewed along with their applications, challenges, and future scope.

Keywords: Deep Learning, Neural Networks, Optimization Techniques, CNN, Artificial Intelligence, Transfer Learning

How to Cite?: Akash Dattatray Raut, "Deep Learning Models and Neural Network Optimization Strategies: A Review", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 586-588, https://www.ijsr.net/getabstract.php?paperid=SR26509130439, DOI: https://dx.dx.doi.org/10.21275/SR26509130439

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