A Deep Dive into Neural Networks: Architectures, Training Techniques, and Practical Implementations


  • Danish Iqbal Department of Computer Science, University of Al Khwarizmi


Neural Networks, Architectures, Training Techniques, Backpropagation, Optimization Algorithms, Regularization, Artificial Intelligence, Practical Implementations


Neural networks, inspired by biological neural systems, have revolutionized the field of artificial intelligence, driving advancements in various domains from image and speech recognition to medical diagnostics and autonomous vehicles. This paper offers an extensive exploration into the intricate world of neural networks, delving deep into their architectures, training methodologies, and real-world applications. Beginning with a foundational overview of neural network structures, the paper progresses to discuss state-of-the-art training techniques, emphasizing the significance of backpropagation, optimization algorithms, and regularization methods. Furthermore, practical implementations across diverse sectors are highlighted, showcasing the transformative potential of neural networks in addressing complex challenges. Through this comprehensive analysis, the paper aims to provide readers with a holistic understanding of neural networks, elucidating their underlying principles and showcasing their profound impact on modern computational paradigms.