The Best Optimization Algorithm for Your Neural Network

Author(s): Riccardo Andreoni

Originally published on Towards AI.

How to choose it and minimize your neural network training time.

Developing any machine learning model involves a rigorous experimental process that follows the idea-experiment-evaluation cycle.

The article discusses various optimization algorithms for training neural networks, focusing on techniques to speed up the training process. It explains the importance of optimizing models, highlights various methods such as Transfer Learning and Batch Normalization, and provides an in-depth analysis of several algorithms including Batch Gradient Descent, Mini-Batch Gradient Descent, Momentum Gradient Descent, RMS Prop, and Adam. The author emphasizes the trade-offs between different optimizers and provides practical insights into their effectiveness based on empirical testing with a neural network trained on the Fashion MNIST dataset.

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Published via Towards AI