Neural Network that classifies images of numbers with 97%+ accuracy, stars:0, update:2024-04-29 23:58:08 (2024-04-30, Jupyter Notebook, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1714435091169937.htmlusing Multiple Linear regression ( R_squared_score = 0.97), stars:0, update:2024-04-20 17:12:29 (2024-04-22, Jupyter Notebook, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1713792460164695.htmlThe project implements a deep learning approach for classifying dermoscopy images, aiding in the diagnosis of melanoma.The proposed method involves the use of a combination of EfficientNet-B0 features, GLCM, LBP, color moments features, and metadata features to improve base model s performance by 93,97% AUC score. (2024-04-16, Jupyter Notebook, 0KB, 下载0次)
http://www.pudn.com/Download/item/id/1713250301311805.html
IIIT-H提供的人工智能统计方法课程第一个作业的解决方案。该作业是关于KNN和决策树实现的。
Solutions for the first assignment of the course Statistical Methods in AI offered at IIIT-H. This assignment is on KNN and Decision Tree implementation. (2024-04-06, Jupyter Notebook, 0KB, 下载0次)
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)
Proyecto de detección de estado de semáforos para ayudar a personas invidentes utilizando IA。Desarrollo de modelos de aprendizaje automático con una precision de hasta el 95-97%。Repositorio con código，视频y presentación disponible。
Proyecto de detección de estado de semáforos para ayudar a personas invidentes utilizando IA. Desarrollo de modelos de aprendizaje automático con una precisión de hasta el 95-97%. Repositorio con código, video y presentación disponible. (2024-04-05, 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)
利用卷积神经网络和TensorFlow实现了一个基于神经网络的系统，以准确预测作物病害。实现了97%的显著病害识别准确率，显著提高了作物产量和可持续性。
Implemented a neural network-based system leveraging Convolutional Neural Networks and TensorFlow to accurately predict crop diseases. Achieved a remarkable disease identification accuracy of 97%, significantly improving crop yield and sustainability. (2024-04-02, Jupyter Notebook, 0KB, 下载0次)
分类器。通过NN头w多5嵌入从SetFit到Mistral微调到延迟优化
Classifier(s). From SetFit to Mistral fine-tuning to latency optimization through NN head w multi-e5 embeddings (2024-03-29, Jupyter Notebook, 0KB, 下载0次)
开发了一个机器学习框架，专门用于从以显著不平衡为标志的数据集中识别最有前途的潜在客户。获得了令人印象深刻的97%的准确率，为销售团队提供了对客户基本属性的宝贵见解，从而扩大了成功销售的可能性。
Developed a Machine Learning framework tailored to discern the most promising prospective customers from a dataset marked by significant imbalances. Attained an impressive accuracy rate of 97%, furnishing the sales team with invaluable insights into essential client attributes, thereby amplifying the likelihood of successful sales endeavors. (2024-03-28, Jupyter Notebook, 0KB, 下载0次)
利用Callbacks建立图像分类模型（Rock-Paper Scissors），训练和验证准确率达到97%以上。基于分词类引入的项目数据科学机器学习
Making image classification model (Rock Paper Scissors) with Callbacks achieved more than 97% training and validation accuracy. Project based on Dicoding Class Introduction Datascience Machine Learning (2024-03-24, Jupyter Notebook, 0KB, 下载0次)
构建预测模型，将法语句子分为不同的难度（A1-C2），并构建迷你Web App，根据输入的关键字和难度返回法语Youtube视频的结果。
Built a prediction model to classify the French sentences into different difficulties (A1-C2), and constructed a mini Web-App to return the results of French Youtube videos based on the input keywords and difficulty. (2024-03-23, Jupyter Notebook, 0KB, 下载0次)
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)
“很高兴与大家分享我在LinkedIn上的最新项目！介绍我在Streamlit上部署的空气污染预测ML模型。该解决方案在培训和测试数据方面的准确率分别为97%和96%，令人印象深刻，专注于关键污染物：二氧化硫、二氧化氮和PM10。记住，当地AQI变化可能会受到因素的影响。
"Excited to share my latest project on LinkedIn! Introducing my air pollution prediction ML model deployed on Streamlit. With an impressive accuracy of 97% on training and 96% on testing data, this solution focuses on key pollutants: Sulfur Dioxide, Nitrogen Dioxide, and PM10. Remember, local AQI variations may be influenced by factor. (2024-03-20, Jupyter Notebook, 0KB, 下载0次)
Slaha97 Aiplanet监督学习回归课程
Slaha97 Aiplanet Supervised learning Regression course (2024-03-17, Jupyter Notebook, 0KB, 下载0次)
创建了一个Naive baye的文本分类模型，对垃圾邮件和火腿进行分类，准确率为97%。
Created a Naive baye s model for text classification that classifies the spam emails and ham with 97% accuracy. (2024-03-10, Jupyter Notebook, 0KB, 下载0次)
MNIST Mastery ANN，精度97.5
MNIST Mastery ANN at 97.5 Accuracy (2024-03-10, Jupyter Notebook, 0KB, 下载0次)
该报告包含一个使用HOG和SVM对CIFAR-10数据集中的图像进行分类的项目。它使用TensorFlow、scikit image、scikit-learn、matplotlib和joblib库。它还演示了预处理、流水线、并行计算以及模型保存和加载技术。在试验数据上达到了62.97%的精度。
This repo contains a project that classifies images from the CIFAR-10 dataset using HOG and SVM. It uses TensorFlow, scikit-image, scikit-learn, matplotlib, and joblib libraries. It also demonstrates preprocessing, pipelining, parallel computing, and model saving and loading techniques. It achieves an accuracy of 62.97% on the test data. (2024-03-10, Jupyter Notebook, 0KB, 下载0次)
使用Kotlin、Python和Jetpack Compose构建的Android应用程序，使用机器学习算法以97%的准确性检测心脏病
An Android application built using Kotlin, Python and Jetpack Compose that uses Machine Learning Algorithm to detect heart disease with 97% accuracy (2024-03-10, 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)