Determining the Optimal Neural Network Architecture and Parameters: A Comprehensive Review and Practical Manual for Data Scientists
Abstract
The performance of a Neural Network (NN) critically depends on its architecture and parameter configuration. Selecting optimal network depth, width, activation functions, and training hyperparameters is a nontrivial task that substantially influences convergence speed, generalization ability, and computational efficiency. Despite significant progress in optimization and Auto Machine Learning (ML) research, a unified practical framework for determining the optimal NN structure for a given dataset remains elusive. This paper presents a comprehensive review of methods for NN optimization, organized across two hierarchical levels: inner-level parameter optimization (weights, biases, learning rates) and outer-level architecture optimization (depth, width, activation functions, and other hyperparameters). We systematically examine manual design heuristics, search-based techniques (grid, random, Bayesian), evolutionary and reinforcement-based Neural Architecture Search (NAS), differentiable NAS, and meta-learning approaches. Emerging trends such as differentiable hyperparameter optimization, continuous architecture representations, and operator-based frameworks are also discussed. In addition to the theoretical synthesis, the paper provides a practical manual for data scientists, detailing a step-by-step workflow to design, train, and refine NNs efficiently. The review concludes with an analysis of current challenges and outlines future research directions toward mathematically grounded, unified frameworks for network optimization.
Keywords:
Neural network, Architecture optimization, Hyperparameter tuning, Neural architecture search, Auto Machine learningReferences
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