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Hello world !

 The story of "Hello, World!" is deeply tied to the history of programming and computer science education. Here's a quick rundown of its origins and significance: 1. Origins in Early Programming The phrase "Hello, World!" first appeared in programming literature in the 1970s. It was popularized by Brian Kernighan in his book The C Programming Language (1978), co-authored with Dennis Ritchie , the creator of the C language. However, Kernighan had already used it in an earlier 1972 internal Bell Labs tutorial for the B programming language, a precursor to C. The first recorded "Hello, World!" example in B looked like this: main() { printf("hello, world\n"); } 2. Why "Hello, World!"? Simplicity : It's a small, easy-to-understand program that demonstrates basic syntax. Testing : It's often the first thing programmers write when learning a new language. Debugging : It ensures that the compiler and environm...
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Android Layout Types – Visual Guide

Android Layouts – Deep Dive Reference Android Layouts — Deep Dive Side-by-side XML & UI · Attributes · Examples · When to use each LinearLayout RelativeLayout ConstraintLayout FrameLayout TableLayout ScrollView GridLayout ⬇️ LinearLayout android.widget.LinearLayout The simplest layout. All children are arranged in a single direction — either vertical (top→bottom) or horizontal (left→right). Each child follows the next in sequence. You can use layout_weight to distribute space proportionally. ✓ Simple & fast ✓ Predictable ✓ Supports weight ✗ No overlap ✗ Deep nesting = slow Best for: forms, toolbars, lists // Key Attributes android:orientation "vertical" | "horizontal" Direction children are stacked. Default is horizontal. ...
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Building an End-to-End MLOps Portfolio Project with CI/CD

Building an End-to-End MLOps Portfolio Project with CI/CD In today's competitive data science landscape, demonstrating MLOps expertise is essential for landing senior roles. This comprehensive guide provides a concrete 8-week plan to build a production-grade machine learning project with complete CI/CD pipelines, automated testing, monitoring, and deployment—showcasing skills that set you apart from candidates who only build Jupyter notebooks. Introduction Most data science portfolios showcase exploratory data analysis and model training, but few demonstrate the ability to deploy and maintain models in production. CI/CD (Continuous Integration/Continuous Deployment) pipelines are critical infrastructure that automates testing, validation, and deployment of machine learning systems, ensuring reliability and reproducibility at scale. This guide presents a complete MLOps project structure focused on building a Sentiment Analysis API with full auto...
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