Python Shallow vs Deep Copy

Is Python still worth learning in 2026? 🤔 Most students think it’s just a “beginner language”… but the truth is far more powerful. Swipe to know the real picture of Python in today’s job market 👉 #Python2026 #IsPythonWorthIt #PythonDeveloper #TechCareer #FutureProofSkills #SCOPE 

🐍 Python for AI -2 (Visual Learning) ♦️ Most people learning AI make this mistake 👇 They jump to models… without understanding data. #ThinkFirst_6 ⚡ Reality: AI is just smart handling of data structures Master these 4 → you’re ahead of 80% beginners. ✨ Major Datatypes - python 💡 Save this - you’ll use it in every project. #FamAI #LearnFirst_BuildSmart #VisualLearning_FamAI #Python 🙂

Learning Python has shown me that coding is not only about syntax — it is about solving problems step by step. One thing I appreciate about Python is how readable and beginner-friendly it is, while still being powerful enough for automation, data analysis, testing, and AI. Today’s reminder: even writing a small script that saves 10 minutes of manual work is progress. Small improvements create big results over time. Currently focusing on growing my skills in Python and automation, one day at a time. 🚀 #Python #CodingJourney #Automation #LearningEveryday #TechSkills #SoftwareTesting #CareerGrowth

Learning Python made me realize something At the beginning, I thought coding was about memorizing syntax… But now I see it’s more about problem-solving and thinking logically. Syntax can always be looked up, but the ability to break down a problem takes real practice. Still on the journey — one step at a time. #Python #Learning #ProblemSolving #GrowthMindset

🚀 Everyone talks about learning Python… But very few actually prepare for real interview questions. Here’s what most people miss 👇 Instead of just watching tutorials, focus on problems like: ✔ Finding largest & smallest elements ✔ Optimizing with single-pass logic ✔ Understanding time complexity (O(n) vs O(n log n)) ✔ Writing clean, efficient Python code Because in interviews… 👉 It’s not about knowing syntax 👉 It’s about how you THINK The difference between average and selected candidates? They practice problems that actually get asked. Start simple: Arrays → Logic building → Optimization → Real-world thinking Consistency beats talent in tech. Every single time. 💡 Tip: Don’t just solve… understand why that solution works What’s one Python question that challenged you the most? 👇 #Python #DataAnalytics #CodingInterview #LearnToCode #100DaysOfCode #Programming #TechCareers #SoftwareDevelopment #AI #MachineLearning #CodingKaro #mdluqmanali

I made a very classic beginner mistake — I underestimated Python. I thought I could skip the "essentials" and jump straight into Machine Learning like I had a mod. But ML doesn’t really do shortcuts. Turns out, it expects you to actually understand what you are doing. After spending time working with fundamentals like lists and dictionaries, I had a simple but uncomfortable realization: If your Python foundation is weak, Machine Learning doesn’t feel advanced— it just feels like you’re constantly struggling to keep up in a language you barely speak. So I’m doing the sensible thing now: going back and strengthening the basics. Yes, the “boring” stuff I tried to rush past. And honestly, Python was never the problem. My overconfidence was. #Python #MachineLearning #ProgrammingBasics #LearnToCode #NeverStopLearning #100DaysOfCode #TechLearning

Most people think Python is "slow" because it's dynamic. I used to think that too. Then I started working on Pyrefly. Static analysis is essentially a "spell-checker" for code. At Meta's scale, we can't wait 20 minutes for a test to run. We need to catch bugs the millisecond they are typed. Working on a Rust-powered engine that processes 1.8 million lines of code per second has taught me one thing: Efficiency isn't a luxury; it's a requirement. I’m learning how to bridge the gap between Python’s flexibility and Rust’s safety. It’s challenging, it’s frustrating, and it’s the most fun I’ve ever had. #RustLang #Python #StaticAnalysis #SystemsEngineering

🚀 Deep Copy vs Shallow Copy in Python — A Small Concept, Big Impact! Today I explored the difference between shallow copy and deep copy in Python, and it’s one of those concepts that can save you from unexpected bugs 👇 🔹 Shallow Copy Creates a new object, but references nested objects Changes in nested elements reflect in the original 🔹 Deep Copy Creates a completely independent copy Changes do NOT affect the original object 💡 Key Takeaway: Use shallow copy when performance matters and shared references are okay. Use deep copy when you need full data isolation. ⚠️ Ignoring this difference can lead to tricky bugs, especially when working with nested data structures. #Python #Programming #Learning #FrontLinesEdutech Sai Kumar Gouru

Ever find yourself writing extra lines just to add data to a dictionary? Checking if a key exists before adding an item gets old. This Python trick automatically initializes your dictionary values. It cleans up your data aggregation and processing loops. ✨ It's a lifesaver for grouping features or metrics in your AI/ML workflows. What's your favorite Python shortcut for cleaning up data processing? #Python #AIDeveloper #MachineLearning #CodingTips #DataScience

“InsightFace Explained: From Images to Embeddings in Python Using Deep Learning” Most tutorials focus on how to use a library. In this article, I focused on something more important: 👉 Understanding how InsightFace works under the hood 👉 How images are converted into embeddings 👉 How face matching actually works 👉 And how to build a simple, production-style pipeline using Django I’ve broken everything down step by step—from uploading a selfie to retrieving matching photos from a database. If you’re working with: Computer Vision Machine Learning Python backend systems Or building real-world AI applications this might be useful for you. Would love your feedback 👇 Here is the link to read my article https://lnkd.in/gARtC9Ng #ArtificialIntelligence #MachineLearning #ComputerVision #Python #DeepLearning