Machine Learning Expedition

Sign in Subscribe

Machine Learning Expedition

Markov Decision Process

Reinforcement learning (RL) is a paradigm of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. The concept of Markov Decision Processes (MDPs) is central to understanding how reinforcement learning works. An MDP provides a mathematical framework for modeling decision-making

Fine-Tuning in Computer Vision: Harnessing the Power of Pre-trained Models

In the dynamic world of computer vision, the concept of 'seeing' and 'interpreting' through the lens of a machine has been a subject of fascination and relentless pursuit. From its inception, where rudimentary forms attempted to mimic the human eye's capabilities, to the present

Hugging Face Transformers for Sentiment Analysis: A Practical Guide

Hugging Face is a popular platform known for its extensive range of tools and libraries related to natural language processing (NLP). The company has gained significant recognition for making cutting-edge machine learning models, especially in the field of NLP, more accessible to the wider community of data scientists, developers, and

Understanding Batch Normalization: Enhancements and Limitations

Batch Normalization (BN) has become a cornerstone technique in deep learning since its introduction by Sergey Ioffe and Christian Szegedy in 2015. Designed to accelerate deep network training, it has evolved over the years, addressing both its inherent limitations and adapting to new challenges in the field. This blog post

Understanding the Limitations and Challenges of Calibrated Classifiers in Machine Learning

Calibrated classifiers are a critical concept in the field of machine learning, especially in the context of probability estimation. The primary goal of a classifier in machine learning is to predict the category or class of given input data. However, in many real-world applications, understanding the confidence of these predictions

How PCA Makes Sense of Your Multitude of Measurements

Principal Component Analysis (PCA) is a powerful statistical technique used for dimensionality reduction in data analysis and machine learning. Its primary objective is to transform a set of possibly correlated variables into a set of linearly uncorrelated variables called principal components. PCA is widely used in fields such as exploratory

Understanding the Difference: Linear Regression vs. Linear Mixed-Effects Models

Understanding the Difference: Linear Regression vs. Linear Mixed-Effects Models

Linear regression and linear mixed-effects models are two statistical methods often used in data analysis, but they serve different purposes and have distinct features. Linear Regression: It's a basic form of statistical analysis, ideal for examining the relationship between two variables. It assumes independence of observations and homogeneity