🐍 “Python is slow.” That’s what beginners say. Yes, pure Python isn’t as fast as C++. But here’s the twist 👇 When you train a model using PyTorch or TensorFlow, you’re not actually running heavy computations in Python. Under the hood: • Core operations are written in highly optimized C++ • GPU acceleration runs through CUDA • Linear algebra is powered by low-level compiled code Python is just the clean interface. The real performance engine is running beneath it ⚙️ 👉This is why I chose Python early in my AI journey — simplicity on top, performance underneath. #Python #ArtificialIntelligence #MachineLearning #DeepLearning
Python Performance: Separating Fact from Fiction
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Mastering machine learning sounds cool until you're buried in math, lost in algorithms, and wondering what Python package you're supposed to install next. If you've ever: - Opened a tutorial and closed it 10 minutes later - Felt like everyone else already gets it - Wondered where you were supposed to start... This blog post can help you. It breaks down the real path to getting started with machine learning using Python. #MachineLearning #Python #AI #DataScience #RheinwerkComputingBlog #RheinwerkComputingInfographic Take your first (or next) step here: https://hubs.la/Q0448D_q0
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Exciting News! 🎉 I'm thrilled to announce the launch of my new Open Source Python library, LLM-TokenCost! 🐍 A lightweight Python library for tracking LLM API costs with budget alerts, spending limits, and detailed per-model breakdowns. Check out the documentation and get started today: ➡️ https://lnkd.in/gUwyxPjM Open to feedback and feature addition #Python #LLM #MachineLearning #APICost #OpenSource #Developer #DataScience #AI
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Today’s Python Focus: Data Types Before building complex AI systems, you must master the basics. Today I covered: ✔ Numeric Types ✔ Strings ✔ Lists & Tuples ✔ Sets ✔ Dictionaries ✔ Type Conversion Strong foundations create strong developers. On to the next concept tomorrow 💪 #Python #FutureEngineer #LearningInPublic #AIJou
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🚀 Python Learning Journey – Revision Day Today, I revised Day 7, Day 8, and Day 9 topics to strengthen my fundamentals. Here’s what I revised: ✅ Functions (def, arguments, return values) ✅ Built-in string functions ✅ List built-in methods and functions Revision helped me understand concepts more clearly and confidently. Practicing consistently and building a strong foundation 💪 Small steps every day lead to big results. #Python #LearningJourney #Revision #Functions #Strings #Lists #KeepLearning
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A quick #map #visualization in #Python cheat sheet on - and if you want to learn all of these (and a lot) more in Python, then: 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 - 𝐒𝐞𝐜𝐨𝐧𝐝 𝐄𝐝𝐢𝐭𝐢𝐨𝐧 The book: https://lnkd.in/dy-7m_zz Sample: https://lnkd.in/dVP-Ty-Y Reviews: https://lnkd.in/dMii-cxX Overview: https://lnkd.in/d5anyYAU
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Python Beyond Syntax: Quantum-Inspired Meta-Structures Modern Python isn’t just about writing code—it’s a medium for designing systems in thought-space. I’ve been exploring a concept I call the “Root Meta-Hierarchy”: Objects (Root) encapsulate state Iterative transformations propagate through nested structures Hierarchical layers operate in binary-aligned, quantum-mediated states, maintaining coherence while enabling self-referential evolution Conceptually, this mirrors a Hamiltonian system, where each state evolves in structured interaction with its hierarchy. The real skill isn’t syntax—it’s architecting computation at the meta-level. #Python #MetaProgramming #QuantumLogic #SystemsDesign #ObjectOrientedThinking
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Learn in Public — Day 11 Today I explored multiple ways to compute the Greatest Common Divisor (GCD) in Python. Implemented several approaches: • Brute force approach using iteration • Recursive subtraction-based GCD • Optimized recursive version • Euclidean algorithm using modulo • Python's built-in math.gcd() function Key takeaway: The Euclidean Algorithm is significantly more efficient than the naive approach because it reduces the problem size quickly using modulo operations. This exercise helped me understand how the same problem can be solved with different algorithmic strategies — each with different time complexities and performance trade-offs. Consistently learning and improving every day. #LearnInPublic #Python #Algorithms #DataStructures #CodingJourney #SoftwareEngineering #ProblemSolving
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The other day I shared about my latest project, Python Mastery. Here is the very first lesson for free. It will give you a GREAT overview of what to expect and how to pace yourself to become a great Python engineer in 8 weeks. From the fundamentals, to the most important libraries, AI/ML, to Web development. Lots to cover, but Python is a wonderful, versatile tool. Any comments or feedback are always appreciated. The course sale ends in just a few days, so don't miss the window - https://lnkd.in/eKUEjvme https://lnkd.in/e4b6Dwm8 #python #ai #coding
What You Need to Know to Master Python (Fundamentals, Libraries, AI, Machine Learning, Backend)
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“Something new I learned in Python today” Even a correct mathematical function can misbehave because of floating-point precision. Replacing: math.ceil(x / d) with (x + d - 1) // d made the solution both safer and faster :) Love how debugging often turns into unexpected learning ;) #Python #CodingJourney #Debugging #LeetCode
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that's called vectorization, it is done in pandas methods too, like valuesCount()