Deep Dive into Regression: Recursive Least Squares Explained (Part 3) Introduction to Recursive Least Squares Ordinary least squares assumes that all data is available at once, but in practice, this isn’t always the case. Often, measurements are obtained sequentially, and we need to update our estimates as new data comes in. Simply augmenting the data matrix $\mathbf{X}$ each time a new measurement arrives can become computationally expensive, especially when dealing with a large number of measurements.

An Introductory Guide (Part 2) Understanding Ridge Regression In machine learning, one of the key challenges is finding the right balance between underfitting and overfitting a model. Overfitting occurs when a model is too complex and captures not only the underlying patterns in the training data but also the noise. This results in a model that performs well on the training data but poorly on new, unseen data. Underfitting, on the other hand, happens when a model is too simple to capture the underlying patterns in the data, leading to poor performance both on the training data and on new data.

An Introductory Guide (Part 1) Even with the rapid advancements in deep learning, regression continues to be widely used across various fields (e.g., finance, data science, statistics, and so on), maintaining its importance as a fundamental algorithm. That’s why I’ve decided to share this post, which is the first article in a dedicated series on regression. This series is designed to provide a thorough review while offering a gentle and accessible introduction.

Securing Your Privacy The importance of securing your data has become critical in the modern digital era. This post explores a versatile tool called GnuPG, or GNU Privacy Guard, which allows you to encrypt your data and communications, ensuring that only the intended recipients can access them. Asymmetric Encryption Before looking at GPG, let’s first review some encryption approaches. A very naive approach to sharing encrypted files is to use the same secret key between a sender and a receiver.

What is TaskSpooler ? TaskSpooler (ts) is a lightweight job scheduler that allows you to queue up your tasks and execute them in order. It’s particularly useful for environments where tasks need to be managed sequentially or with a controlled degree of parallelism. Unlike more complex systems like SLURM, TaskSpooler is designed for simplicity and ease of use, making it accessible for individual researchers and small teams. Efficient Job Scheduling for ML/DL Researchers with Taskspooler In the dynamic field of Machine Learning (ML) and Deep Learning (DL), managing and optimizing computational resources is crucial.

Introduction Poetry is a dependency management and packaging tool in Python, aiming to improve how you define, install, and manage project dependencies. Installation: You can install Poetry through its custom installer script or using package managers. The recommended way is to use their installer script to ensure you get the latest version. Creating a New Project: Use poetry new <project-name> to create a new project with a standard layout. Adding Dependencies: Add new dependencies directly to your project using poetry add <package>.