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garg-aayush/README.md

Hi 👋🏽, I'm Aayush

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  • 💼   Building AI-driven products to solve real-world problems
  • 🚀   Specializes in production generative AI systems, multi-agent systems, and AI image enhancement workflows
  • 🎨   Experience with diffusion Models, VLMs/LLMs, LoRA finetuning, custom ComfyUI pipelines & nodes and ML deployments
  • 🧱   Currently building LLM components from first principles: building-from-scratch
  • 🎓   Background in computational (geo)-physics

I'm always eager to connect with fellow devs. Please feel free to reach out!

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  1. building-from-scratch building-from-scratch Public

    Python 62 10

  2. spatial-alias-removal spatial-alias-removal Public

    Removing the spatial aliasing in the seismic data using Deep Learning Super-resolution

    Python 22 3

  3. research-papers research-papers Public

    Contains the summaries and notes on a variety of DL papers/blogs

    HTML 12

  4. finance_dashboard_example finance_dashboard_example Public

    Finance dashboard example using Plotly Dash

    Python 11 1

  5. Managing multiple CUDA versions usin... Managing multiple CUDA versions using environment modules in Ubuntu
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    # Steps to manage multiple CUDA environments
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    > Latest Update: May 19th, 2024
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    This gist contains all the steps required to:
    
              
    
          
    
    
    
  
    

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        Step-by-Step Guide to setup your own...  Step-by-Step Guide to setup your own personal GPU server      
    

          
    
              
    
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    # Setting Up Your Personal GPU Server: A Step-by-Step Guide
    
              
    
              
    
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    I've been using a GPU workstation with an RTX 4090 for almost a year now, and it's been one of the best decisions I've made. With a personal GPU server, you no longer need to rely on cloud-based GPU instances from services like `RunPod` or `Vast.ai` every time you want to run a job or try new models. The best part? No stress about recurring GPU instance costs! :-)
    
              
    
              
    
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    However, I rarely work directly on my workstation. Instead, I prefer the flexibility of accessing the GPU remotely using my MacBook, whether I'm working from different locations within my home, from a co-working space, or a cozy cafe in another part of town. 
    
              
    
          
    
    
    
  
    

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