The complete, friendly deep-dive into the core linear algebra every Data Scientist needs. 1 Introduction Have you ever wondered how to unlock the secrets hidden in data but felt overwhelmed by the math? This article provides a friendly introduction to eigenvalues and eigenvectors which is a crucial linear algepra topic as it serves as the
1 Introduction Linear transformations and Change of Basis are deceptively simple concepts that tempt us to rush over them quickly. But this shortcut is the perfect way to frustration. The gaps in our understanding will backfire and cost us valuable time later. There is no free lunch; we have to take the time to build
Mastering the Math Behind AI, Machine Learning and Data Science Introduction Matrix multiplication is the workhorse of the modern world. From powering deep learning algorithms to rendering photo-realistic graphics in video games — it’s everywhere. A solid understanding of this process is fundamental to anyone working with data. Unfortunately, we are often taught just one way to perform
The method of maximum likelihood estimation allows to estimate point parameters for a given distribution underlying some observed data. Let’s look at an example to understand what this means:Imagine you measure the height of 20 women within an age-range between 20-40 years. You denote the measurements as \(X_1=x_1, X_2=x_2,…,X_{20}=x_{20}\), where the capital \(X_i\) refers to
