I’ve been trying out different Python libraries for number crunching, and the right choice can make a big difference. NumPy – solid for general calculations on the CPU. JAX – works on GPU and can handle automatic calculations for machine learning. CuPy – best for heavy calculations that need GPU speed. They all look similar, but each works best in different situations. Which one do you use most? #Python #DataScience #MachineLearning #NumericalComputing

Just wrapped up a real-time person tracking project using YOLOv11 + OpenCV. The model detects and tracks multiple people in video streams, assigning unique IDs and drawing motion trails for each person. Built and optimized in Python, leveraging CUDA acceleration for fast GPU inference. It also integrates non-maximum suppression (NMS) to refine detections and avoid duplicate bounding boxes. Excited about how far real-time vision systems have come — from detection to robust tracking in just a few lines of code! Check out the results below : #ComputerVision #DeepLearning #YOLOv11 #Python #AIEngineering #OpenCV #Tracking #ObjectDetection

Stepping deeper into the world of Computer Vision — exploring how machines interpret and understand images. This project focuses on Edge Detection using Python and OpenCV, a fundamental concept in image processing that allows algorithms to identify object boundaries and key visual features. It’s fascinating to see how a simple grayscale image transforms into structured data through filters and gradient operations! #MachineLearning #ComputerVision #ImageProcessing #Python #DataScience #Upskilling

"Diving into the fundamentals of computer vision. This project uses Python and OpenCV to perform contour detection and draw bounding boxes around objects in an image. #ComputerVision #Python #OpenCV #ObjectDetection #ImageProcessing #Tech"

🔢 Creation of Arrays using NumPy In this practical, I explored how to create and manipulate arrays efficiently using NumPy in Python. Learned different methods to create arrays such as array(), arange(), zeros(), ones(), and linspace() — essential for numerical computing and data manipulation tasks. 📘 Guided by: Ashish Sawant 💻 GitHub: 👉 [https://lnkd.in/dFff8cPb] #DataScience #NumPy #Python #MachineLearning #Coding #Array #PracticalLearning #DataScienceLab

✨🎉 Latest Article! 🎉✨ This #TechThursday, explore how short Python simulations help students unlock deeper intuition in probability—five Python simulations reveal probabilistic ideas students rarely grasp from theory alone. #STEM #EdTech💡 Read here: https://lnkd.in/eKP_8cjn

📉 Experiment 5 – Creation of Arrays using NumPy In this practical, I learned how to create and manipulate arrays using Python’s NumPy library. Created 1D, 2D, and matrix arrays to understand how NumPy helps in handling numerical data efficiently. This experiment gave me a clear idea of how arrays form the foundation for data analysis and scientific computing in Python. 📁 GitHub:https://lnkd.in/eTtC53qu 🎓 Guided by: Ashish Sawant #Python #NumPy #Array #DataScience #MachineLearning #Coding #Learning #JupyterNotebook #CSE#PRMCEAM

Data scientists spend over 70% of their time collecting and wrangling data. #IntelXeonW processors help teams reclaim their workday with up to 60 cores built to boost NumPy and SciPy operations by up to 2x. Explore how the Xeon W processor's vectorized operations can transform your data prep in our solution brief: https://intel.ly/47jblUQ #DataScience #IntelAI #Python #IntelInside