🧠 Python + Edge = Smarter systems. Learn how to deploy ML models on embedded devices using TensorFlow Lite. #EdgeAI #PythonML #EmbeddedIntelligence

Python launched its latest 3.14 version — and it brings two major updates that change everything! 🚀 💡 1️⃣ No more GIL (Global Interpreter Lock) Earlier, Python could only run one thread at a time, even on multi-core CPUs. With Python 3.14, GIL is finally optional, meaning: ✅ True multi-threading ✅ Faster performance for AI, ML, and data-heavy tasks ✅ Better use of all CPU cores 🧵 2️⃣ T-Strings (Template Strings) A new way to handle strings! t"Hello {name}" doesn’t just print text — it keeps the structure of your template, making string handling safer, cleaner, and more powerful for developers. 👉 Does this change make your coding life easier? Let me know in the comments below 👇 #python314 #AIML #pythonupdate #linkedin

Hello Everyone 👋 , 🤔 Is your Python code running slower than you expected? That’s because Python runs on a single core by default. But when your task is CPU-bound (like heavy calculations or simulations), you can unleash your system’s full potential using #ProcessPoolExecutor from concurrent.futures. This method distributes tasks across multiple CPU cores, enabling true parallel processing — and cutting execution time dramatically. ✅ Ideal for: CPU-heavy operations ❌ Avoid for: I/O-bound work (use ThreadPoolExecutor instead) 🔹 Just wrap your loop or function inside ProcessPoolExecutor() and experience the performance boost. Simple. Powerful. Efficient. 💪 Checkout attached docs for example. #contact: navinkpr2000@gmail.com #Python #ProcessPoolExecutor #Concurrency #ParallelComputing #Multiprocessing #Performance #Optimization #CodeTips #PythonDevelopers #Programming #AI #MachineLearning #DataScience #TechCommunity #Developers #crewxdev

GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator https://lnkd.in/e6Cu5MfR #python #rstats

We’ve generated vertex-lit meshes in webGL from HDF5 data for clients, enabling the live visualisation of complex datasets in a clear, detailed 3D format. Simplifying the complex through innovation! https://lnkd.in/e2UdqjYX #3DModeling #HDF5 #unity3d #python #data #webgl

Tired of manually sifting through hours of video just to grab a few key frames? I just finished automating a seriously painful process: extracting precise, key screenshots from long videos. This is a game-changer for anyone dealing with quality control, documentation, or video analysis. I break down the entire step-by-step process in my new video, showing exactly how I harnessed the power of: 🐍 Python: For scripting the logic. 📸 OpenCV: To handle the frame-by-frame video processing. No more tedious scrubbing! Automate the extraction, focus on the insights. Check out the full tutorial below! https://lnkd.in/dM5JYnN7 #Automation #PythonProgramming #OpenCV #VideoAnalysis #TechTutorial #DataScience #Productivity

Ever wondered why NumPy is so fast? It’s not “just C under the hood.” It’s SIMD — your CPU’s secret weapon. Python lists are flexible but slow. NumPy, on the other hand, can process millions of elements in milliseconds. How?? -SIMD Vectorization — one instruction, many data points -Contiguous Memory Layout — perfect for CPU caching -Compiled C Loops — no interpreter overhead -BLAS/LAPACK Optimization — parallel, cache-tuned math -Loop Fusion — fewer memory passes NumPy = Pythonic syntax + C-speed execution. Read my full breakdown on https://lnkd.in/ggEUeGjJ #AI #Python #Numpy #SIMD #CPU #Performance #DataScience #MachineLearning

🚶♂️ Pedestrian Detection using Python & OpenCV – My New Project! I’ve recently built a Pedestrian Detection System using OpenCV and Haar Cascade Classifier. This project detects people walking in a video by processing each frame in real time. 🛠️ Technologies Used: • Python • OpenCV • Haar Cascade Classifier • VS Code 🎯 What I learned: • How video frames are processed • Real-time object detection • Using Haar Cascades for human detection • Working with computer vision algorithms This project helped me understand the basics of Computer Vision and how detection models work. 🙏 A special thanks to Kodi Prakash Sir for his continuous support and guidance during my learning journey. #python #opencv #pedestriandetection #computervision #machinelearning #learning #projects #codingjourney

𝗗𝗮𝘆 𝟯𝟵 𝗼𝗳 #𝟭𝟴𝟬𝗗𝗮𝘆𝘀𝗢𝗳𝗖𝗼𝗱𝗲 Today, I built on the first/last occurrence solution to 𝗰𝗼𝘂𝗻𝘁 𝗼𝗰𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝗲𝘀 𝗼𝗳 𝗮 𝘁𝗮𝗿𝗴𝗲𝘁 𝗶𝗻 𝗮 𝘀𝗼𝗿𝘁𝗲𝗱 𝗮𝗿𝗿𝗮𝘆 𝘄𝗶𝘁𝗵 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀. Using the same lower_bound and upper_bound helpers: Lower bound → first index where element ≥ target Upper bound → first index where element > target The count is simply: count = upper_bound - lower_bound This gives an O(log n) solution — much faster than scanning the entire array, especially with many duplicates. It’s a great example of how breaking a problem into reusable pieces leads to clean and efficient code. Perfect for analytics, frequency analysis, and search optimizations! 📊 #Python #Algorithms #BinarySearch #FrequencyCount #Coding #ProblemSolving