Optimization Methods in Deep Learning: A Comprehensive Overview

TL;DR

This paper provides a comprehensive overview of optimization methods in deep learning, covering first-order, momentum-based, and adaptive techniques, along with challenges and best practices for various tasks.

Contribution

It offers a detailed synthesis of optimization algorithms, challenges, and practical recommendations, serving as a valuable reference for researchers and practitioners.

Findings

Summarizes key optimization algorithms and their properties.

Discusses challenges like vanishing gradients and convergence issues.

Provides practical guidelines for selecting optimization methods.

Abstract

In recent years, deep learning has achieved remarkable success in various fields such as image recognition, natural language processing, and speech recognition. The effectiveness of deep learning largely depends on the optimization methods used to train deep neural networks. In this paper, we provide an overview of first-order optimization methods such as Stochastic Gradient Descent, Adagrad, Adadelta, and RMSprop, as well as recent momentum-based and adaptive gradient methods such as Nesterov accelerated gradient, Adam, Nadam, AdaMax, and AMSGrad. We also discuss the challenges associated with optimization in deep learning and explore techniques for addressing these challenges, including weight initialization, batch normalization, and layer normalization. Finally, we provide recommendations for selecting optimization methods for different deep learning tasks and datasets. This paper serves as a comprehensive guide to optimization methods in deep learning and can be used as a reference for researchers and practitioners in the field.