Principal Component Analysis (PCA) Explained

What is PCA? Let’s say that you want to predict what the gross domestic product (GDP) of the United States will be […]

What are eigenvectors and eigenvalues?

Introduction Eigenvectors and eigenvalues have many important applications in computer vision and machine learning in general. Well known examples are PCA […]

Automatic Differentiation Explained

Introduction There are several methods to calculate gradients in computer programs: (1) Manual differentiation; (2) Symbolic differentiation; (3) Finite differences […]

Improvements in Deep Q Learning: Dueling Double DQN, Prioritized Experience Replay, and fixed Q-targets

Deep Q-Learning was introduced in 2014. Since then, a lot of improvements have been made. So, today we’ll see four […]

Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) Simply explained

Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) estimation are method of estimating parameters of statistical models. Despite a […]

Implicit Recommender Systems with Alternating Least Squares

http://activisiongamescience.github.io/2016/01/11/Implicit-Recommender-Systems-Biased-Matrix-Factorization/ In today’s post, we will explain a certain algorithm for matrix factorization models for recommender systems which goes by […]

How to determine epsilon and MinPts parameters of DBSCAN clustering

Every data mining task has the problem of parameters. Every parameter influences the algorithm in specific ways. For DBSCAN, the […]

A review on Deep learning based recommendation systems

Source: https://jameskle.com/writes/rec-sys-part-2 INTRODUCTION The number of research publications on deep learning-based recommendation systems has increased exponentially in the past recent […]