Mar 3, 2025 · In machine learning, optimizers and loss functions are two fundamental components that help improve a model’s performance. A loss function evaluates a model's effectiveness by computing the difference between expected and actual outputs. Common loss functions include log loss, hinge loss, and mean square loss.

May 28, 2024 · Optimization algorithms are the backbone of machine learning models as they enable the modeling process to learn from a given data set. These algorithms are used in order to find the minimum or maximum of an objective function which in …

Optimization Algorithms are used to minimize or maximize an objective (loss) function. In the context of machine learning and optimization problems, we use them to iteratively adjust the parameters of a model to minimize a loss function and improve the model’s performance.

Dec 30, 2023 · One of the most common algorithms performed during training is backpropagation consisting of changing weights of a neural network in respect to a given loss function. Backpropagation is usually performed via gradient descent which tries to converge loss function to a local minimum step by step.

Apr 25, 2024 · Deep learning optimization algorithms, like Gradient Descent, SGD, and Adam, are essential for training neural networks by minimizing loss functions. Despite their importance, they often feel like black boxes. This guide simplifies these algorithms, offering clear explanations and practical insights

Jul 6, 2024 · To address the limitations of gradient descent, several advanced optimization algorithms have been developed specifically for deep learning. These algorithms aim to accelerate the training process...

In this chapter, we explore common deep learning optimization algorithms in depth. Almost all optimization problems arising in deep learning are nonconvex. Nonetheless, the design and analysis of algorithms in the context of convex problems have proven to be very instructive.

4 days ago · In this article, we will take a look at various optimization algorithms and see how the choice of learning rate is an important hyperparameter in optimization. Understanding The Different Optimizers. We can now look at the different optimization algorithms — and then afterward, and then try to understand when and why to use each.

Apr 30, 2025 · Traditional operational research methods have been the primary means of solving combinatorial optimization problems (COPs) for the past few decades. However, with the rapid increase in the scale of problems in real-world scenarios and the demand for online optimization, these methods face persistent challenges including computational complexity and optimality. In recent years, combinatorial ...

Jan 17, 2025 · This study aims to organize a wide range of existing data optimization methodologies for deep learning from the previous literature, and makes the effort to construct a comprehensive taxonomy for them. ... (L2O) is a growing field that employs a variety of machine learning (ML) methods to learn optimization algorithms automatically from data ...