A multi-level perceptron neural network made from scratch, using NumPy and SkLearn. Implements backpropagation and utilized on the mnist dataset as a test case. Obtains acc > 97% test accuracy. *Some functions from BU ECE department and imported libraries. (2024-04-21, Python, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1713668519998242.html
探索分类器（XGBoost、CatBoost、LightGBM、Bagging、ExtraTrees）预测中风的功效。ExtraTrees:99.8%，打包：98.5%，CatBoost:97.7%，XGBoost:96.3%，LightGBM:91.8%。
Exploring efficacy of classifiers (XGBoost, CatBoost, LightGBM, Bagging, ExtraTrees) for predicting stroke. ExtraTrees: 99.8%, Bagging: 98.5%, CatBoost: 97.7%, XGBoost: 96.3%, LightGBM: 91.8%. (2024-04-06, Jupyter Notebook, 0KB, 下载0次)
使用TensorFlow和Keras的神经网络从MNIST数据集中分类手写数字，实现97%的准确性。
A neural network with TensorFlow and Keras to classify hand-written digits from the MNIST dataset, achieving 97% accuracy. (2024-04-02, Jupyter Notebook, 0KB, 下载0次)
BF计算器
BF calculator (2024-02-29, HTML, 0KB, 下载0次)
建立了一个基于TensorFlow的CNN，在RSI-CB256卫星图像分类上实现97%的准确性！这表明了深度学习从卫星图像中提取有意义信息的能力。
Built a TensorFlow-based CNN achieving 97% accuracy on RSI-CB256 satellite image classification! This demonstrates the power of deep learning for extracting meaningful information from satellite imagery. (2024-02-02, Jupyter Notebook, 0KB, 下载0次)
使用Streamlit应用程序构建预测机器学习模型来预测每周销售。模型实现了97.4%的准确性，并使用EDA分析了趋势、模式和数据见解。比较各种功能，并确定对价格有重大影响的关键因素。
Built a predictive machine learning model using a Streamlit application to predict weekly sales. Model achieved 97.4% accuracy and analyzed trends, patterns, and data insights using EDA. Compared various features and identified key contributors with a significant impact on prices. (2024-01-22, Jupyter Notebook, 0KB, 下载0次)
钻石价格预测器-Web应用程序：使用各种回归模型预测钻石价格：线性回归、Lasso、Ridge、ElasticNet、决策树回归、随机森林回归和KNeighbors回归。所选的随机森林回归器以97%的卓越精度部署在用户友好的烧瓶应用程序中
Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app (2024-01-10, Jupyter Notebook, 0KB, 下载0次)
该项目使用TensorFlow框架构建的深度学习模型来检测X射线图像中的肺炎。模型架构基于EfficientNetB7模型，该模型在我们的测试数据上实现了大约97%（96.96%）的准确性。这种高准确率是我们人工智能模型的优势之一。
This project uses a deep learning model built with the TensorFlow framework to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97% (96.96%) on our test data. This high accuracy rate is one of the strengths of our AI model. (2023-12-22, Jupyter Notebook, 0KB, 下载0次)
耦合a1
coupled a1 (2022-04-28, Python, 0KB, 下载0次)
这是一款电子邮件SMS垃圾邮件检测应用程序，使用多标称朴素贝叶斯模型进行预测，准确率为97.0%。
This is a Email SMS spam detection app that uses Multinominal Naive Bayes model to make prediction with an accuracy score of 97.0%. (2023-10-11, Python, 0KB, 下载0次)
本项目基于Masure等人的工作([https: github.com LoicMasure ASCADv2Code- Analysis)对ASCADv2-Extracted数据集进行了泄露检测和高阶相关能量分析。](https: github.com LoicMasure ASCADv2Code- Analysis%EF%BC%89%E5%AF%B9ASCADv2-Extracted%E6%95%B0%E6%8D%AE%E9%9B%86%E8%BF%9B%E8%A1%8C%E4%BA%86%E6%B3%84%E9%9C%B2%E6%A3%80%E6%B5%8B%E5%92%8C%E9%AB%98%E9%98%B6%E7%9B%B8%E5%85%B3%E8%83%BD%E9%87%8F%E5%88%86%E6%9E%90%E3%80%82),
Based on the work of Masure et al. ([https: github. com LoicMasure ASCADv2Code - Analysis), this project has carried out leak detection and higher-order correlation energy analysis on the ASCADv2 Extracted dataset.] (https: github.com LoicMasure ASCADV2Code - Analysis% EF% BC% 89% E5% AF% B9ASCADV2-Extracted% E6% 95% B0% E6% 8D% AE% E9% 9B% 86% E8% BF% 9B% E8% A1% 8C% E4% BA% 86% E6% B3% 84% E9% 9C% B2% E6% A3% 80% E6% B5% 8B% E5% 92% 8C% E9% AB% 98% E9% 98% B6% E7% 9B% B 8% E5% 85% B3% E8% 83% BD% E9% 87% 8F% E5% 88% 86% E6% 9E% 90% E3% 80% 82), (2023-09-30, Python, 0KB, 下载0次)
该GitHub存储库展示了一个机器学习项目，重点是使用随机森林回归模型将鸢尾花分类为三个物种。该项目包括数据清理、可视化、超参数调整和模型评估，结果是令人印象深刻的97.77%的准确性。
This GitHub repository showcases a machine learning project focused on classifying Iris flowers into three species using a Random Forest Regressor model. The project includes data cleaning, visualization, hyperparameter tuning, and model evaluation, resulting in an impressive 97.77% accuracy. (2023-09-19, Others, 0KB, 下载0次)
Protocolo usados en la Tesis de Maestria（2017）
Protocolos usados en la Tesis de Maestría (2017) (2022-02-19, JavaScript, 0KB, 下载0次)
Código-referente ao artigo[https:medium.com@lauangermandi criando uma-camada de cache para-requisi%C3%A7%C3%B5es-...]
Código referente ao artigo https://medium.com/@lauanguermandi/criando-uma-camada-de-cache-para-requisi%C3%A7%C3%B5es-http-com-net-5-refit-e-decorator-com-scrutor-6f0e921ed1c8 (2021-11-14, C#, 0KB, 下载0次)
计算机科学12-A1项目
Computer Science 12 - A1 Project (2017-10-21, Python, 0KB, 下载0次)
AAAI2022 安全AI挑战者计划第八期:Data-Centric Robust Learning on ML Models 0.97+方案之一
AAAI2022 Security AI Challenger Plan Phase VIII: Data Centric Robust Learning on ML Models 0.97+Solution (2022-01-24, Python, 32KB, 下载0次)
大数据A1:Unigram生成器
Big Data A1 : Unigram generator (2016-09-07, Java, 16KB, 下载0次)
使用Go开发《基金数据分析系统》,项目系统框架是基于gin-api-template的基础上做二次开发,gin-api- template源码地址:[https: github.com 52lu gin-api- template,《基金...](https: github.com 52lu gin-api- template%EF%BC%8C%E3%80%8A%E5%9F%BA%E9%87%91%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E7%B3%BB%E7%BB%9F%E3%80%8B%E8%AE%A1%E5%88%92%E5%AE%8C%E6%88%90%E5%8A%9F%E8%83%BD%EF%BC%8C%E4%B8%BB%E8%A6%81%E6%9C%89)
Use Go to develop the Fund Data Analysis System. The project system framework is based on the gin api template for secondary development, Gin api template Source code address: [https: github.com 52lu gin api template, Fund...] (https: github.com 52lu gin api template% EF% BC% 8C% E3% 80% 8A% E5% 9F% BA% E9% 87% 91% E6% 95% B0% E6% 8D% AE% E5% 88% 86% E6% 9E% 90% E7% B3% BB% E7% BB% 9F% E3% 80% 8B% E8% AE% A1% E5% 88% 92% E5% AE% 8C% E6% 88% 90% E5% 8A% 9F% E8% 83% BD% EF% BC% 8C% E4% B8% BB% E8% A6% 81% E6% 9C% 89) (2021-11-08, GO, 83KB, 下载0次)
利用Python进行数据分析([https: read.douban.com reader ebook 15249337 )源代码整理版](https: read.douban.com reader ebook 15249337 %EF%BC%89%E6%BA%90%E4%BB%A3%E7%A0%81%E6%95%B4%E7%90%86%E7%89%88)
Data analysis using Python ([https: read.douban.com reader ebook 15249337) source code collation) (https: read.douban.com reader ebook 15249337% EF% BC% 89% E6% BA% 90% E4% BB% A3% E7% A0% 81% E6% 95% B4% E7% 90% 86% E7% 89% 88) (2018-12-31, Jupyter Notebook, 35601KB, 下载0次)
计算机科学作弊表A0海报。
A Computer Science Cheat Sheet A0 Poster. (2018-03-05, TeX, 143KB, 下载0次)