I work as a software engineer specializing in backend development, DevOps, and machine learning.
My side projects, resources for the courses I develop, and thoughts on numerous things
that interest me may all be found here.
About This Site
Welcome to my personal website. This platform serves as a comprehensive showcase of my work in software engineering and a resource for fellow developers and tech enthusiasts. My journey in the tech world has been driven by a passion for creating innovative solutions and a commitment to continuous learning and improvement.
On this site, you will find a curated selection of my projects, technical writings, and open source contributions. Each section is designed to provide insight into my skills, experiences, and the impact of my work in the technology industry.
Navigating the Site
Explore the various sections to learn more about my work and expertise:
Blog - Dive into my thoughts, insights, and updates on various projects and technology trends. Here, I share detailed posts on software development, best practices, and industry news.
Tools - Discover a range of open-source tools and software I have developed.
Projects - Browse through an overview of my current and past projects.
Resume - Review my professional background, skill set, and experiences. This section highlights my career journey, educational background, and key accomplishments.
Available Blog Categories
My blog is categorized into various topics to help you find the information you are interested in. Below are the main categories available:
English
Algorithms and Data Structures:
definitions of common algorithms (sorting, searching, graph) and data structures (lists, trees, hashes) with code examples.
Numerical Methods:
algorithms for root finding, integration, differential equations, with step-by-step implementations.
Statistics Notes:
explanations of probability distributions, hypothesis tests, regression techniques, illustrated by sample datasets.
Stanford Machine Learning:
organized lecture notes covering linear/logistic regression, neural networks, support vector machines, with sample code.
Numpy Tutorials:
examples of array creation, indexing, broadcasting, linear algebra routines and performance tips.