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Learning materials, Quizzes & Assignment solutions for the entire IBM data science professional certification. Also included, a few resources that I found helpful.
A powerful data & AI notebook templates catalog: prompts, plugins, models, workflow automation, analytics, code snippets - following the IMO framework to be searchable and reusable in any context.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
AssemblyNet: 3D Whole Brain MRI segmentation pipeline
[MICCAI 2019] [MEDIA 2020] Models Genesis
Official Keras & PyTorch Implementation and Pre-trained Models for Semantic Genesis - MICCAI 2020
Context Axial Reverse Attention Network for Small Medical Objects Segmentation
Open source platform for the machine learning lifecycle
Official PyTorch implementation of SegFormer
Documentation for Ross Wightman's timm image model library
reproduction of semantic segmentation using masked autoencoder (mae)
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Awesome List of Mixup Augmentation Papers for Visual Representation Learning
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
Reading list for research topics in Masked Image Modeling
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
Simple MAE (masked autoencoders) with pytorch and pytorch-lightning.
PraNet: Parallel Reverse Attention Network for Polyp Segmentation, MICCAI 2020 (Oral). Code using Jittor Framework is available.