SupriyaSubash

  ARTIFICIAL INTELLIGENCE ........ITS VERY    UNIQUE IN WHOLE WORLD 

NOTE : We're using an Nvidia GPU as an example here, but you can follow the same steps for AMD and Intel GPUs, too.   STEPS :   1.Open the Start menu and click on Settings   2.Select System from the left sidebar menu and choose Display. 3.Select the Graphics option that's located under Related settings. 4.Select Desktop app in the dropdown menu and click on Browse. 5.Head into the directory where you installed Visual Studio Code and select it. 6.With Visual Studio Code selected, click Options to select the preferred graphics processor for this application. 7.Select High performance and click Save.

A graphics card is one of the most important and expensive components that goes into a new PC build. It's commonly used to render graphics for video games, but you can use it for various other tasks, including video editing, 3D model rendering, machine learning, and more. GPUs typically have higher processing throughput compared to CPUs, which is why they're also commonly used for processing in various source code editors like Visual Studio Code (VS Code). VS Code, in particular, uses your graphics card's resources for various things like GPU environment management, usage tools, debugging support, and more. That's why it's important to set up the VS Code interface properly, ensuring it gets access to your GPU and its resources.

            Very Useful Information     In the dynamic landscape of data science, Python remains the go-to programming language for its versatility and extensive ecosystem of libraries. As we step into 2024, the Python data science toolkit continues to evolve, with new libraries and updates enhancing the capabilities of professionals in the field. 1. TensorFlow 2.x 2. PyTorch 3. Pandas 4. Scikit - Learn 5. Dask 6. Statsmodels 7. Matplotlib and Seaborn 8. XGBoost 9. NLTK (Natural Language Toolkit) 10. Plotly