Deconstructing Deep Learning: An Analysis of its Intellectual Architecture and Methodological Evolution

Abstract

Deep learning has revolutionized fields such as computer vision and natural language processing, yet its theoretical foundation remains an area of active research. This paper provides a comprehensive review of the core theories underpinning deep learning, encompassing the principles of neural networks, the backpropagation algorithm, and the expressive characteristics of deep architectures. We analyze four prominent neural network architectures: feedforward neural networks, which serve as the foundational framework; convolutional neural networks (CNNs), which achieve breakthroughs in image processing through localized receptive fields; recurrent neural networks (RNNs), which excel in sequence modeling; and Transformers, which leverage self-attention mechanisms to enhance performance across diverse tasks. Furthermore, we systematically trace the evolution of optimization algorithms, from stochastic gradient descent (SGD) to advanced variants such as Adam, and discuss regularization techniques including Dropout and batch normalization. The paper also examines emerging trends, such as the rise of large-scale pre-trained models and frontier technologies like model compression. However, we highlight persistent challenges, including the escalating demand for computational resources and the inherent lack of interpretability in deep learning models. Addressing these issues is crucial for advancing the field and ensuring its sustainable development.