Month: February 2024

Navigating the Optimization Landscape: The Do’s and Don’ts of choosing an Optimization Problem

Selecting the right optimization problem is crucial for solving complex challenges, involving the adjustment of model parameters to optimize an objective function in machine learning. Mathematical and computational techniques aim to find the best solution from a set of feasible ones, focusing on objective functions, decision variables, and constraints. Optimization enhances machine learning models through training, hyperparameter tuning, feature selection, and cost function minimization, directly affecting accuracy and performance. This process necessitates an understanding of problem specifics, appropriate metric selection, and computational complexity consideration, while avoiding pitfalls like unclear objectives and overlooking real-world constraints.

Comparative Analysis of Random Search Algorithms

Introduction Local Search Algorithms play a crucial role in Machine Learning by addressing a wide range of optimization problems, as noted by Solis and Wets [1]. These algorithms are especially useful for tasks like hyperparameter optimization or optimizing loss functions. Search algorithms are particularly beneficial in situations where computational resources are limited or the problem […]

Simulated Annealing : Methods and Real-World Applications

Optimization techniques play a critical role in numerous challenges within machine learning and signal processing spaces.This blog specifically focuses on a significant class of methods for global optimization known as Simulated Annealing (SA). We cover the motivation, procedures and types of simulated annealing that have been used over the years. Finally, we look at some real world applications of simulated annealing, not limited to the realms of Machine Learning, demonstrating the power of this technique.

Tutorial on Hyperparameter Tuning Using scikit-learn

Introduction Hyperparameter tuning is a method for finding the best parameters to use for a machine learning model. There are a few different methods for hyperparameter tuning such as Grid Search, Random Search, and Bayesian Search. Grid Search is a search algorithm that performs an exhaustive search over a user-defined discrete hyperparameter space [1, 3]. […]

Transfer Learning for Boosting Neural Network Performance

Transfer learning is a machine learning technique that utilizes a model already trained for one task on another separate, related task. In this article, we will take a deep dive into what this means, why transfer learning has become increasingly popular to boost neural network performance, and how you can use transfer learning on your […]