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Working from home
19 followers · 1 following

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Achievement: StarstruckAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

Achievements

Achievement: StarstruckAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

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greatsharma/README.md

Hi there 👋 💻

👦This is Gaurav Sharma, a skilled AI engineer with experience in building real-time computer-vision applications for scalable and reliable business. Experienced with SOTA architectures, libraries and tools used in the AI industry. A competitive performer who believes in both exploration and exploitation and is capable of delivering the best under challenging conditions. Apart from this I am active on GitHub and have contributed to some well-known ML libraries. I am also a kaggle kernel expert.

📝Resume

Languages, Frameworks and Tools

Some of my Blogs

And many more ...

Connect with me

💬 Ask me anything anytime, I am happy to help if I can :)
📫Reach me: [email protected]

  • 🌱 I’m currently exploring the field of computer vision.
  • ⚡ Pro Tip: Master dictionary both of english and python, it really helps a lot :)

Github Stats

Gaurav's github stats

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Featured Repos




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  1. SVD-Image-Compression SVD-Image-Compression Public

    A jupyter notebook showing how images are compressed using Singular Value Decomposition in Python

    Jupyter Notebook 2 2

  2. Particle-Filter Particle-Filter Public

    Python

  3. Custom-HaarCascade-FrontalFace Custom-HaarCascade-FrontalFace Public

    A custom made haar cascade for the frontal face using OpenCV.

    Jupyter Notebook 4

  4. Binary-Search-Tree Binary-Search-Tree Public

    A C++ implementation of Binary Seach Tree.

    C++ 1

  5. Space Optimal Fuzzy Matching: ... Space Optimal Fuzzy Matching: This gist do a fuzzy matching of documents on the basis of levenshtein distance and returns a similarity score. This do the same as my previous gist https://gist.github.com/greatsharma/eeb22285a837a9b29431179451d0ba7f with a time complexity of O(mn) but the space complexity is now reduced to O(m) i.e., linear time, where m and n are documents length which we match.
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          Adjacency Graph in C++
Adjacency Graph in C++      
    

          
    
              
    
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    #include <stack>
    
              
    
              
    
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