ML THeory A1,, stars:0, update:2024-05-05 15:57:47 (2024-05-06, Jupyter Notebook, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1714927786252431.html
利用XLMRtransformer模型的最先进的阿拉伯语词性标记器，令人印象深刻的测试精度为97.49%，在阿拉伯语UD Treebank上的F1测试分数为96.44%。
A state-of-the-art Arabic part-of-speech tagger leveraging the XLMR transformer model With an impressive testing accuracy of 97.49% and a remarkable testing F1-score of 96.44% on the Arabic UD Treebank. (2024-03-22, Jupyter Notebook, 0KB, 下载0次)
CS6910-深度学习基础课程的作业
Assignments of Course CS6910- Fundamentals of Deep Learning (2024-03-08, Jupyter Notebook, 0KB, 下载0次)
沪深4107 A1
CSI4107 A1 (2024-02-12, Jupyter Notebook, 0KB, 下载0次)
A1 AIE22134 V1.ipynb
A1 AIE22134 V1.ipynb (2024-02-10, Jupyter Notebook, 0KB, 下载0次)
A1 AIE22108 V1.ipynb版本
A1 AIE22108 V1.ipynb (2024-02-04, Jupyter Notebook, 0KB, 下载0次)
A1 AIE22105 V1.ipynb版本
A1 AIE22105 V1.ipynb (2024-02-04, Jupyter Notebook, 0KB, 下载0次)
SYNC INTERN S实习First Task是一种植物病害检测人工智能模型，能够以97%的准确率检测38种不同的病害
SYNC INTERN S internship First Task which is a Plant Disease Detection AI model that detects 38 different disease with 97% accuracy (2024-01-23, Jupyter Notebook, 0KB, 下载0次)
使用python解决问题-赋值1
Problem solving with python - assignment 1 (2024-01-20, Jupyter Notebook, 0KB, 下载0次)
本项目是参加飞桨常规赛:中文场景文字识别的项目,项目score为85.94141。 生成的预测文件为work中的result.txt文件 项目任务为识别包含中文文字的街景图片,准确识别图片中的文字 本项目源于[https: aistudio.baidu.com aistud...](https: aistudio.baidu.com aistudio projectdetail 615795%EF%BC%8C%E5%9C%A8%E6%AD%A4%E5%9F%BA%E7%A1%80%E4%B8%8A%E8%BF%9B%E8%A1%8C%E4%BF%AE%E6%94%B9)
This project is to participate in the flying oar regular race: Chinese scene character recognition project, and the project score is 85.94141. The generated prediction file is the result.txt file in the work. The project task is to identify the street view pictures containing Chinese characters, and accurately identify the characters in the pictures. This project originates from [https: aistudio.baidu.com aistud...] (https: aistudio.baidu.com aistudio projectdetail 615795% EF% BC% 8C% E5% 9C% A8% E6% AD% A4% E5% 9F% BA% E7% A1% 80% E4% B8% 8A% E8% BF% 9B% E8% A1% 8C% E4% BF% AE% E6% 94% B9) (2023-12-11, Jupyter Notebook, 0KB, 下载0次)
以%97的准确性检测52000篇假新闻，
Detecting Fake News with 52,000 article with %97 Accuracy, (2020-02-01, Jupyter Notebook, 0KB, 下载0次)
反向传播（Back Propagation）
Back Propagation (2023-11-18, Jupyter Notebook, 0KB, 下载0次)