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AmeerHamza111
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21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
Start your development with a Design System for React-Native inspired by Soft UI Design System.
Text documentation for various languages, frameworks, applications.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
A self-supervised learning framework for audio-visual speech
Hydra is a framework for elegantly configuring complex applications
PyTorch implementation of a 1.3B text-to-image generation model trained on 14 million image-text pairs
PromptBERT: Improving BERT Sentence Embeddings with Prompts
AmeerHamza111 / ConvNeXt
Forked from facebookresearch/ConvNeXtCode release for ConvNeXt model
It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced research…
Repository of continual learning papers
Dense Unsupervised Learning for Video Segmentation (NeurIPS*2021)
10 Weeks, 20 Lessons, Data Science for All!
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Simple image captioning model
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers https://arxiv.org/abs/2109.08535
A game theoretic approach to explain the output of any machine learning model.
C++ tensors with broadcasting and lazy computing
Documentation and issues for Pylance
📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Python Automation Cookbook, published by Packt