Chenwei Zhang's starred repositories
screenshot-to-code
Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
leedl-tutorial
《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
Awesome-Diffusion-Models
A collection of resources and papers on Diffusion Models
Deep-Learning-Interview-Book
深度学习面试宝典(含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向)
NLP_ability
总结梳理自然语言处理工程师(NLP)需要积累的各方面知识,包括面试题,各种基础知识,工程能力等等,提升核心竞争力
open_flamingo
An open-source framework for training large multimodal models.
PerceptualSimilarity
LPIPS metric. pip install lpips
Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
improved-diffusion
Release for Improved Denoising Diffusion Probabilistic Models
UNetPlusPlus
[IEEE TMI] Official Implementation for UNet++
Graphormer
Graphormer is a general-purpose deep learning backbone for molecular modeling.
alphafold2
To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
2024-Tech-OA
List of Tech Company OAs. Save your time from finding them all over the internet.
pytorch-nested-unet
PyTorch implementation of UNet++ (Nested U-Net).
MachineLearningFAQ
Machine Learning FAQ
model-angelo
Automatic atomic model building program for cryo-EM maps
Pras_Server
Pras Server is a library to repair PDB or mmCIF structures, add missing heavy atoms and hydrogen atoms and assign secondary structure by amide-amide interactions of the backbone
Papers-atomic-model-building-in-CryoEM-maps
This repository lists the state-of-the-arts methods by using machine and deep learning for protein atomic model building from cryoEM density maps.
osfclienttutorial
This Jupyter notebook is a tutorial on the osfclient module (https://github.com/osfclient/osfclient).
fast_dijkstra
The script defines a function dijkstra_n_shortest_paths that efficiently computes a specified number of shortest paths in a weighted graph using Dijkstra's algorithm, returning the paths as an array of tuples. https://anaconda.org/ChenweiZhang/fast_dijkstra