Choosing the right Python library for number crunching: NumPy, JAX, CuPy

View the entire Machine Learning process on my portfolio: https://lnkd.in/dMvszHyk Still on the Time Series Forecasting with real dataset gotten from kaggle.com How do you train your model quicker when you on CPU and not GPU for a very large dataset, because I’ve been training this model for over 18mins even with EarlyStopping. #MachineLearning #DataScience #Python #ModelTraining #DataAnalyst

🚀Excited to share my 5th Python practical! 💻 This practical focused on the creation of arrays using NumPy, one of the most powerful libraries in Python for numerical computing. I learned how to efficiently create, manipulate, and explore different types of arrays — an essential step toward mastering data analysis and scientific computation. 📁 Here's the Google drive : linkhttps://lnkd.in/gxfhQ8cB 🔗GitHub account : https://lnkd.in/gcCiRDfS #Python #DataAnalysis #NumPy #LearningJourney #CentralTendency

🔢 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

When Python runs faster than C++ - meet Codon. Codon is a high-performance Python compiler that transforms your scripts into native machine code, running 10–100× faster than CPython. No GIL. Real multithreading. Even GPU acceleration. And the best part? It keeps the same Python syntax - just turbo mode engaged. 🐍💨 If performance ever held your Python back - Codon changes the game. Link in comments👇 #Python #DevOps #AI #MachineLearning #HPC #Performance #SoftwareEngineering

📉 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

Python 3.14 finally brings a no-GIL version, which means threads can now run in true parallel and use all CPU cores. I feel this could really help frameworks like Frappe, faster background jobs, better use of CPU, and less memory load since we may not need too many worker processes. But I’m curious to know what others think will this actually make a big difference in real-world Frappe performance? #Python #NoGIL #Frappe #Performance

🔢 Experiment 5: Creating of Dataframe using NumPy ⚙️ In this lab, I explored the core concepts of Data Frame creation and manipulation using NumPy, one of the most essential Python libraries for numerical computing. 🔍 Key learning outcomes:  • Creating 1D, 2D, and multi-dimensional arrays  • Understanding array attributes and indexing  • Leveraging NumPy for efficient mathematical and statistical computations This practical helped me understand how NumPy arrays form the foundation for most data manipulation, analysis and machine learning tasks in Python. 📁 Explore the repository here : 👉https://lnkd.in/epWys7e7 #DataScience  #Python  #NumPy  #MachineLearning  #DataAnalysis  #DataScienceLearning  #JupyterNotebook  #LearningJourney Ashish Sawant sir

✨🎉 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

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