rainyren Goto Github PK

azure-feedback

chatgpt-on-wechat

基于大模型搭建的聊天机器人，同时支持 微信公众号、企业微信应用、飞书、钉钉 等接入，可选择GPT3.5/GPT-4o/GPT4.0/ Claude/文心一言/讯飞星火/通义千问/ Gemini/GLM-4/Claude/Kimi/LinkAI，能处理文本、语音和图片，访问操作系统和互联网，支持基于自有知识库进行定制企业智能客服。

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

hw_model

hyperlearn

50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

iot

lunglobeseg

For paper of Lung lobe segmentation

models

Models and examples built with TensorFlow

mpflr

To investigate the optimal machine learning model for predicting the clinical outcomes of MPFLR and to identify the critical predictive factors.

myo-python-gui

radiomics_ovary

radiomics analysis in ovary

template_shr

We have created the in vivo symmetric multi-contrast MRI brain templates and atlas for spontaneously hypertensive rats named the Heibei Medical University rat template set (HRT). Structural T2WI, DTI and resting-state functional MRI (BOLD) scans that were used to build the template set were obtained from 8 SHRs longitudinally scanned in vivo at 10, 24 and 52 weeks of age. HRT comprised T2WI, FA, MD, raw DWI with a b-value of 0 s/mm2 (B0) and BOLD templates; tissue probability maps of gray matter, white matter and cerebrospinal fluid; and a whole-brain atlas with 163 labels. The cortical atlas was derived from the Tohoku atlas, and the subcortical regions were manually delineated. HRT was up-sampled to an isotropic resolution of 1 mm (after scaling 10). We hope that the HRT can serve as a beneficial tool for precise analysis of the SHR brain using structural and functional MRI, which can promote neuroimaging studies on essential hypertension. Number of patent application: 202110462436.2