Setting Up Experiment Environments: A Challenge for Arch Linux Users If you’ve ever tried to set up a new experiment environment for deep learning on Arch Linux, you’re probably familiar with the challenges involved. Arch Linux, renowned for its rolling-release model and cutting-edge updates, provides unparalleled flexibility and control over your system. However, this same flexibility can often lead to headaches when setting up complex environments for machine learning or deep learning experiments.

For the past few months, I’ve been working on an exciting internal project at my company: taking users’ documents and running them through LLM APIs to translate and summarize their content, somewhat similar to DeepL . The output is a collection of translated documents, each overlaid with the newly translated text. Our goal is to provide a stable service that can handle large files efficiently for thousands of employees at Samsung—no small task!

I recently bought a mini PC because I wanted a lightweight machine that I can easily carry anywhere. Arch Linux’s minimalistic, rolling-release approach aligns perfectly with my love for a Vim-based workflow and a highly customizable setup. While the process can seem intimidating at first, it’s an incredibly rewarding experience that offers complete control over your system. Installing Arch Linux (UEFI or BIOS) Arch Linux is well-known for giving users full control over their system.

Introduction to Asymmetric Kernels Recall that the dual form of LS-SVM is given by \begin{align*} \begin{bmatrix} 0 & y^T \\ y & \Omega + \frac{1}{\gamma} I \end{bmatrix} \begin{bmatrix} b \\ \alpha \end{bmatrix} = \begin{bmatrix} 0 \\ e \end{bmatrix} \end{align*} An interesting point here is that using an asymmetric kernel in LS-SVM will not reduce to its symmetrization and asymmetric information can be learned. Then we can develop asymmetric kernels in the LS-SVM framework in a straightforward way.

Introduction to Least-Square SVM Introduction Least Squares Support Vector Machine (LS-SVM) is a modified version of the traditional Support Vector Machine (SVM) that simplifies the quadratic optimization problem by using a least squares cost function. LS-SVM transforms the quadratic programming problem in classical SVM into a set of linear equations, which are easier and faster to solve. Optimization Problem (Primal Problem) \begin{align*} &\min_{w, b, e} \frac{1}{2} \lVert w\rVert^2 + \frac{\gamma}{2} \sum_{i=1}^N e_i^2,\\ &\text{subject to } y_i (w^T \phi(x_i) + b) = 1 - e_i, \ \forall i \end{align*} where:

Support Vector Machine Introduction Support Vector Machines (SVMs) are among the most effective and versatile tools in machine learning, widely used for various tasks. SVMs work by finding the optimal boundary, or hyperplane, that separates different classes of data with the maximum margin, making them highly reliable for classification, especially with complex datasets. What truly sets SVMs apart is their ability to handle both linear and non-linear data through the kernel trick, allowing them to adapt to a wide range of problems with impressive accuracy.