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Hi šŸ‘‹, I'm Ameer Azam

A passionate learner from India

ameerazam08

ameerazam08

  • šŸ”­ Iā€™m currently working as Data Scienctist

  • šŸŒ± Iā€™m currently learning LLMs And Multi Models

  • šŸ“ I regularly write articles on medium (https://medium.com/@ameerazam08)

  • šŸ’¬ Ask me about **Python related ML,DS **

  • šŸ“« How to reach me [email protected]

  • šŸ“„ Know about my experiences

  • āš” Fun fact Getting Error 404 while Internet Connection off

Connect with me:

AZAM

Azam's Twitter Azam's Linkdein AZam's Github pythoguard's Youtube

Languages and Tools:

arduino

c flask heroku

mysql opencv python pytorch scikit_learn tensorflow

ameerazam08

Ā ameerazam08

Ameer Azam's Projects

talkinggaussian icon talkinggaussian

[arXiv] TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting

titanic-machine-learning-from-disaster icon titanic-machine-learning-from-disaster

Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics

top-cvpr-2023-papers icon top-cvpr-2023-papers

This repository is a curated collection of the most exciting and influential CVPR 2023 papers. šŸ”„ [Paper + Code]

torchgan icon torchgan

Research Framework for easy and efficient training of GANs based on Pytorch

torchparse icon torchparse

PyTorch Model Parser: Easily define models in .cfg file(s)

transformers icon transformers

šŸ¤— Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

udifftext icon udifftext

UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models

upscale-a-video icon upscale-a-video

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution

v4 icon v4

Fourth iteration of my personal website

vasa-1-hack icon vasa-1-hack

Using Claude Opus to reverse engineer code from VASA white paper - WIP - (this is for La Raza šŸŽ·)

video-retalking icon video-retalking

[SIGGRAPH Asia 2022] VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild

voodoo3d-official icon voodoo3d-official

Official implementation for the paper "VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment"

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