• MALVINDER SINGH

Neural networks have emerged as powerful tools for various machine learning tasks, exhibiting remarkable capabilities in pattern recognition, classification, and generation of complex data. Neural Networks with their ability to capture temporal dependencies hold immense potential for modeling complex sequential data. This research paper delves into the foundational principles and practical applications of basic neural networks, elucidating their role in modern machine learning paradigms. The research explores the working principle for Feed Forward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks. The research explores key concepts including gradient descent optimization, backpropagation, and regularization techniques, shedding light on their impact on model convergence and generalization. This paper serves as a comprehensive guide for practitioners and enthusiasts alike, fostering deeper insights into the fundamentals of neural network theory and practice.

Convolutional Neural Networks, Feed Forward Neural Networks, Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks.

"EXPLORING THE WORKING PRINCIPLES OF PROMINENT NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.g172-g178, April-2021, Available :http://www.jetir.org/papers/JETIR2105951.pdf

"EXPLORING THE WORKING PRINCIPLES OF PROMINENT NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppg172-g178, April-2021, Available at : http://www.jetir.org/papers/JETIR2105951.pdf