Mastering While Loops in Python for Math Problems

When I first started learning Object-Oriented Programming, I found it tough to connect the theory with practice. Concepts like encapsulation and polymorphism felt abstract and confusing, but breaking them down into simple definitions helped me finally understand them. Here’s my quick summary of the six OOP pillars in Python: Class → A blueprint to create class that defines attributes and methods. Object → An instance created from a class. Inheritance → Reusing parent class properties and methods in child classes. Polymorphism → Same method/operator behaving differently across objects. Encapsulation → Binding data and methods together while controlling access. Abstraction → Hiding internal details and exposing only the essentials. 📌 Sharing this because I know many learners face the same initial confusion. Hopefully, this visual + summary helps simplify the journey! 👉 I used AI tools to design the visual cheat sheet, but the breakdown and explanations are from my own learning journey. Combining tech +  clarity makes concepts easier to share. 👉 Which OOP concept do you find most useful in real-world projects? #Python #OOP #Programming #Learning #CheatSheet #DataScience

🧮 Built a Set Expression Simplifier Desktop Application in Python Set theory is a core concept in Discrete Mathematics, Computer Science, and Logic Design — but simplifying complex set expressions manually can be time-consuming and error-prone. To make this process interactive and efficient, I developed a Set Expression Simplifier Desktop Application using Python. 🔧 Tech Stack: Python Tkinter (GUI Development) Native Python Set Operations 📌 What the Application Does: ✔ Define Universal Set and subsets (A, B, C) ✔ Input set expressions ✔ Perform Union ( | ), Intersection ( & ), Difference ( - ) ✔ Handle Complement ( A′ ) ✔ Display simplified results instantly Example supported expression: (A | B) & C′ The system evaluates expressions using Python’s built-in set operators, ensuring accurate and fast computation. 💡 Why This Matters Set operations form the foundation of: Database queries Logic circuit design Algorithm development Probability theory Data filtering in data science This project demonstrates: Mathematical logic implementation GUI-based desktop application development Practical use of Python data structures Educational software design Next upgrades I’m exploring: Step-by-step simplification explanation Venn diagram visualization Support for more sets Boolean algebra expansion If you're passionate about mathematics, computer science fundamentals, or educational tools, I’d love to connect and exchange ideas. #Python #DiscreteMathematics #ComputerScience #Logic #Programming #EdTech #SoftwareDevelopment https://lnkd.in/gHfMTFYB https://lnkd.in/gEuY7Rwv

🚀 Solved the 4Sum Problem using Python Excited to share another step in my Data Structures & Algorithms learning journey! In this problem, the goal is to find all unique quadruplets in an array that sum up to a given target value. 🔹 Approach Used: • Sorted the array to simplify comparisons • Used two nested loops to fix the first two numbers • Applied the two-pointer technique to find the remaining two numbers • Used a set to store unique quadruplets and avoid duplicates 💡 Key Concepts Practiced: ✔ Arrays ✔ Two Pointer Technique ✔ Sorting ✔ Hash Set for Unique Results ✔ Problem Solving & Optimization 📌 Example Output: [[-2, -1, 1, 2], [-1, 0, 0, 1], [-2, 0, 0, 2]] Practicing problems like this helps strengthen logical thinking and prepares for coding interviews. Looking forward to solving more DSA problems and improving every day! 💻 #Python #DSA #ProblemSolving #Coding #Programming #DataStructures #Algorithms #LeetCode #LearningJourney 10000 Coders Manoj Kumar Reddy Parlapalli Vamsi Enduri GALI VENKATA GOPI

𝗪𝗵𝘆 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 ‘𝗔’ 𝗖𝗮𝗻 𝗖𝗵𝗮𝗻𝗴𝗲 𝗛𝗼𝘄 𝗬𝗼𝘂 𝗧𝗵𝗶𝗻𝗸 𝗔𝗯𝗼𝘂𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 Sometimes the smallest concepts reveal the deepest ideas in computing. Take a simple character like 'A'. On the surface, it’s just a letter. But inside a computer, it tells a much deeper story about how programming actually works. Here are a few interesting things behind it: 1️⃣ 𝗔𝗦𝗖𝗜𝗜 & 𝗕𝗶𝗻𝗮𝗿𝘆 Every character is represented by a number. For example, 'A' = 65, which in binary becomes 01000001. Computers don’t really understand letters — everything is processed as 𝟬𝙨 𝙖𝙣𝙙 1𝙨. 2️⃣ 𝗧𝗵𝗲 𝟯𝟮 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗨𝗽𝗽𝗲𝗿𝗰𝗮𝘀𝗲 𝗮𝗻𝗱 𝗟𝗼𝘄𝗲𝗿𝗰𝗮𝘀𝗲 'A' = 65 'a' = 97 The difference is 32, which means 𝙤𝙣𝙡𝙮 𝙤𝙣𝙚 𝙗𝙞𝙩 𝙘𝙝𝙖𝙣𝙜𝙚𝙨. Because of this design, the case of a character can be toggled using a simple XOR operation: chr(ord('A') ^ 32) # → 'a' chr(ord('a') ^ 32) # → 'A' A clever design choice from the 1960s that still works on modern computers. 3️⃣ 𝗠𝗲𝗺𝗼𝗿𝘆 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: 𝗖 𝘃𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 In C, 65 and 'A' are stored as the same bits in memory — the meaning depends on how the program interprets them. In Python, even storing 65 creates an object with metadata like reference count, type information, and the value itself. This makes Python easier and safer to use, though slightly heavier internally. 4️⃣ 𝗗𝗼𝗲𝘀 .𝗹𝗼𝘄𝗲𝗿() 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗕𝗶𝘁 𝗧𝗿𝗶𝗰𝗸? Not entirely. Python strings support Unicode, so .lower() relies on lookup tables to correctly convert characters across many languages. However, implementations still optimize common ASCII cases internally. 𝙏𝙝𝙚 𝙩𝙖𝙠𝙚𝙖𝙬𝙖𝙮 Some of the most powerful insights in programming come from very simple things. Understanding binary, ASCII, and memory representation helps build a deeper intuition about how computers actually work. #Python #Programming #ComputerScience #DSA #CodingFundamentals #LearningInPublic

The next programming language is Natural Language? 🤔 If we look at the history of computer languages—from raw machine code up to Python—every new language was just adding a higher layer of abstraction to make our lives easier. Now, AI agents are about to be the ultimate abstraction layer. Soon, "coding" will just be aggressively negotiating with an AI until your app works. Give it six months before we are fiercely debating SOLID principles for Prompt Engineering. And a year later, installing Nuget packages of Prompt libraries 😅 The good news? No more missing semicolons. The bad news? We actually have to learn how to communicate clearly now. 🤭 Thoughts? #AI #Agentic #Programming

Every identical sub-expression in tinygrad shares the exact same object in memory. 𝘡𝘦𝘳𝘰 𝘥𝘶𝘱𝘭𝘪𝘤𝘢𝘵𝘦𝘴. 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘤. The secret? A Python metaclass that intercepts object creation. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗲𝗮𝗹 UOp is the core IR node in tinygrad. Every operation, every constant, every buffer reference is a UOp. And UOps are globally deduplicated via a weak reference cache in the metaclass. When you create UOp(ADD, float32, (a, b)), the metaclass checks: does an identical UOp already exist? If yes, return the existing one. If no, create it and cache it. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 👉🏽 UOpMetaClass overrides __call__ 👉🏽 Key = (op, dtype, src, arg, tag) 👉🏽 Lookup in a global dict of weak references 👉🏽 Hit? Return existing UOp. Miss? Create and cache. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁 👉🏽 Structural sharing: identical sub-graphs share nodes automatically 👉🏽 Comparison is pointer equality (𝘪𝘴 instead of ==), which is O(1) 👉🏽 Smaller graphs = faster compilation 👉🏽 Weak references let unused nodes get garbage collected This is Lisp-style symbol interning applied to compiler IRs. It's a technique from programming language design that most ML frameworks miss entirely. 𝘏𝘢𝘷𝘦 𝘺𝘰𝘶 𝘦𝘷𝘦𝘳 𝘶𝘴𝘦𝘥 𝘮𝘦𝘵𝘢𝘤𝘭𝘢𝘴𝘴𝘦𝘴 𝘧𝘰𝘳 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘵𝘩𝘪𝘴 𝘤𝘭𝘦𝘷𝘦𝘳? #tinygrad #ai #python #deeplearning #compiler #opensource

I almost wasted days debugging something that had nothing to do with my actual code. Here's what happened. I was setting up an AI project, installing libraries like ChromaDB, LangChain, and SentenceTransformer and things kept breaking in ways that made no sense. Figured out? I was running Python 3.14. And most AI/ML libraries haven't caught up yet. ChromaDB doesn't fully support it. SentenceTransformer has issues. The errors weren't in my code, they were in the version. Then i fixed this by switching to Python 3.11. Python 3.11 is currently the sweet spot for AI development: ChromaDB is fully supported LangChain is fully tested SentenceTransformer is works perfectly Streamlit runs smoothly Faster than Python 3.10 due to internal optimizations Here's the lesson nobody tells you in tutorials: Newer is not always better in AI/ML development. Stability beats novelty when you're building real products. I didn't learn this from watching videos. I learned it by actually building. There's a difference between knowing Python and knowing how Python behaves inside a real AI stack. You only get that from projects. If you're building anything with AI/ML right now, check your Python version before you write a single line of code. It might save you hours. #Python #AIEngineering #MachineLearning #LLM #RAG #BuildInPublic #AIDevTips #SoftwareEngineering #LearnByDoing

##I've written my own AI Chatbot, and your students can too!## Tired of AI lessons that are all talk and no code? 🤖🐍 I’ve just released a new KS4/GCSE Computer Science pack on TES that bridges the gap between AI theory and Python programming. The highlight: A "Markov Chain" Python lab where students build their own AI 'brain'. I’ve included a custom biased dataset so students can see exactly how training data creates algorithmic bias—a perfect tie-in for OCR J277/H446 ethics. ✅ School-Proof: Includes "Safe Save" logic so it runs on restricted student drives without PermissionErrors. ✅ Exam-Ready: OCR-style 8-mark questions, mark schemes, and model answers included. The link to the resource is in the first comment below #ComputerScience #EdTech #GCSESuccess #ComputingTeacher #PythonProgramming

🚀 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐭𝐚𝐫𝐭𝐬 𝐖𝐢𝐭𝐡 𝐓𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 Python remains one of the most powerful and beginner-friendly programming languages today. Whether you're entering tech, data science, automation, or AI, Python is often the first step. A structured learning path can make the journey much easier. Here’s a simple Basic → Intermediate Python roadmap highlighted in the resource: 🔹 1. Introduction & Setup Start by understanding Python fundamentals and setting up your development environment. Explore official documentation and beginner courses to build a strong base. 🔹 2. Practice With Simple Programs Learning Python becomes easier when you start solving small problems such as: • Printing "Hello, World" • Calculating the sum of two numbers • Converting Celsius to Fahrenheit 🔹 3. Build Logical Thinking Practice questions help develop problem-solving skills: • Calculate the area of a rectangle • Create greeting programs using user input • Check if a number is even or odd • Find maximum and minimum values in a list 💡 The key insight: Programming is not just about syntax. It is about thinking logically, solving problems, and practicing consistently. Professionals who invest time in mastering Python today position themselves for opportunities in data science, automation, AI, and software development. Start small. Practice daily. Build projects. Your Python journey compounds faster than you expect. 👉🏻 follow Alisha Surabhi for more such content 👉🏻 PDF credit goes to the respected owners #Python #Programming #DataScience #Coding #TechSkills #Learning #AI

I spent weeks trying to explain context windows to people until I picked up a paper towel tube and held it to my eye. "See? That's what the AI sees." Instant understanding.