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 […]
Introduction Welcome! This blog post will serve as your introduction to Machine Learning in Python. This guide is designed to set you up to use many of the foundational tools and resources you will use during your time in OMSCS 7641. This post is intended to be a practical crash course introduction to setting up […]
Introduction In the fascinating world of neural networks, activation functions play a pivotal role. They introduce non-linearity into an otherwise linear model, enabling neural networks to learn complex patterns and solve a wide range of tasks. In this article, we will explore the state-of-the-art activation functions, diving into their history, characteristics, and trade-offs. By the […]