Autoencoders - Samenvattingen, Aantekeningen en Examens

What is PCA? (5 key points) Principal Component Analysis is a statistical technique used for dimensionality reduction, crucial when dealing with high-dimensional data in machine learning. It works by transforming original variables into new ones, called principal components, which are linear combinations of the original variables. Key Points: 1. Principal Components: Principal components are the directions in the data that maximize variance. The first principal component captures the most...

INTRODUCTION Currently, about fifty percent of the world’s population lives in urban areas and the forecast is that by 2050 this percentage will grow to approximately seventy percent [10]. As such, the greatest wave of city migration is yet to come and together with it a wide range of challenges raised by the need to improve the style and quality of life of a growing urban population. According to [10], a better understanding of city dynamics would allow for improved services as well as m...

This is a summary of the paper Masked Autoencoders Are Scalable Vision Learners for the course Seminar of Computer Vision by Deep Learning in TU Delft

As a 1st Class Machine Learning student, I've navigated through the fundamental concepts and techniques in our Machine Learning course at King's College London. The course begins with an "Introduction to Machine Learning," where we cover the basics of algorithms learning from data to make predictions without explicit programming. Key areas include "Supervised Learning" such as "Regression" and "Classification," where models learn from labeled data to predict continuous or categorical o...