Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until they converge to a solution that accurately models the data. This convergence process is crucial but can be extremely slow, taking days or weeks to complete with standard methods. Speeding up convergence enables quicker, more efficient neural network training; this allows these powerful models to be rapidly deployed for time-sensitive applications like self-driving cars, medical imaging, and fraud detection. Researchers have developed numerous innovative techniques to accelerate neural network convergence. This article compares popular methods of fast convergence methods for neural networks and provides insights into the most promising approaches for expedited training among the diverse options available.
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