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

   Python 72 11

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

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

   Python 23 3

3. research-papers research-papers Public

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

   HTML 12

4. handbook handbook Public

   A repo contains the different tutorials I write for my use and to share with others.

   Shell 9 3

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:

6. 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.