Yet another developer who is passionate about computer programming, reverse engineering, and machine learning. This blog aims to share the experience, knowledge, papers, and valuable resources.
I have read, watched, and been through all of the articles I create, so if there are any questions related to them, feel free to comment on the underlying page or Contact Me.
For a long time, I had one simple question:
Why can I control my Sennheiser headphones properly from mobile, but not from Windows?
Sennheiser Smart Control is available for iOS and Android, but there is no dedicated Windows desktop control app for everyday headphone control. So I decided to build one myself.
I started SmartControl-Desktop, an unofficial Windows desktop client for supported Sennheiser headphones. At this stage, it supports Sennheiser Momentum 4 on Windows.
Repo: ymg2006/SmartControl-Desktop
This was the start of my journey in C#; it was delightful to code with standard design patterns and make scalable software.
Design patterns are solutions to software design problems you find again and again in real-world application development. Patterns are about reusable designs and interactions of objects.
Before diving into this article, it is highly recommended to look at Unified Modeling Language cheat sheet.
As we know, ML.NET is an open-source and cross-platform (Windows, macOS, Linux) machine learning framework in which you can create custom ML models using C# or F# without having to leave the .NET ecosystem.
ML.NET lets reusing all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps.
Moreover, it has been designed as an extensible platform so that you can consume other popular ML frameworks (TensorFlow, ONNX, Infer.NET, and more) and have access to even more machine learning scenarios, like image classification, object detection, and more.
Finally, according to Microsoft's tests, it has high performance and accuracy.
Using a 9GB Amazon review data set, ML.NET trained a sentiment analysis model with 95% accuracy. Other popular machine learning frameworks failed to process the dataset due to memory errors. Training on 10% of the data set, to let all the frameworks complete training, ML.NET demonstrated the highest speed and accuracy.
I will discuss the fundamentals of ML.NET, explore some sample codes, and explain the basics of the Microsoft Machine-Learning framework with a sample code.
These are a compilation of publicly shared files of Do-Factory Gang Of Four, O'reilly Head First Design Patterns, Refactoring-Guru dive Into design patterns, and dive Into refactoring books with C# example projects.
This article is a concise image of application layers and four golden rules for keeping design patterns in mind while developing codes in general.
C# and Python are one of the several popular programming languages because they are easy to learn, simple to use; moreover, they offer quality, performance, and fast development. Both C# and Python are object-oriented programming languages that aim to make them practical for real-world applications. However, some critical differences between them can help you decide which one is better for you to learn and use. This article discusses the differences between C# and Python by explaining when to use them and how they perform.
The equation is a vital part of many technical manuscripts, including thesis and research papers. However, typing it in Microsoft Word is an arduous task. To cope with it, Microsoft Word introduced the LaTeX type equation editor feature which is termed "Math AutoCorrect" and is available since Microsoft Word 2007.
This article aims to elaborate on the most usages of the Microsoft Word equation editor.
A complete set of tables for writing in LaTeX which comprises:
The categories included in the TCL command list as a cheat sheet is presented below: