Introduction Reinforcement Learning (RL) is a powerful subset of machine learning where agents interact with an environment to hone their decision-making skills. At the core of RL lie Markov Decision Processes (MDPs), providing a mathematical structure to define states, actions, rewards, and the dynamics of how an environment transitions over time. OpenAI Gym has become […]
In this era of machine learning and data analysis, the quest to understand complex relationships within high-dimensional data like images or videos is not simple and often requires techniques beyond simple ones. The patterns are complex, twisted and intertwined, defying the simplicity of straight lines. This is where non-linear manifold learning algorithms step in. They […]
History of Clustering The history of clustering algorithms dates back to the early 20th century, foundationally originating in the realms of anthropology and psychology in the 1930s [1, 2] . It was introduced in anthropology by Driver and Kroeber in 1932 [3] to simplify the ambiguity of empirically based typologies of cultures and individuals. In […]
Introduction Dimensionality reduction can be a critical preprocessing step that transforms a dataset’s features with high dimensions in input space to much lower dimensions in some latent space. It can bring us multiple benefits when training the model including avoiding the curse of dimensionality issues, reducing the risk of model overfitting, and lowering the computation […]
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.
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 […]
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.
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]. […]
Introduction In the field of machine learning, much of the focus and tutorials available are on the creation of a specific model and going from start to finish as if the process were a linear path. In reality, the process is more like a hike where you may find yourself exploring different paths just to […]
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 […]