LoRA E5,, stars:10, update:2024-05-08 23:28:37 (2024-05-09, Jupyter Notebook, 0KB, 下载0次)
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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)
使用Jupyter笔记本开发了SMS垃圾邮件分类器，准确率为97.97%。分类器通过从训练数据中学习，将文本消息识别为垃圾邮件或ham并进行分类。
Developed an SMS spam classifier using Jupyter Notebook with an accuracy rate of 97.97%. The classifier identifies and categorizes the text messages as either spam or ham by learning from the training data. (2024-03-08, Jupyter Notebook, 0KB, 下载0次)
使用NLP单词嵌入模型的搜索引擎：SkipGram和GloVe。
Search Engine using NLP word embedding models: SkipGram and GloVe. (2024-01-25, Jupyter Notebook, 0KB, 下载0次)
为Kaggle源SMS数据设计了NLP管道，通过机器学习实现98%的准确性，通过深度学习实现97%的准确性。实现了高级文本预处理，展示了NLP和机器学习方面的专业知识。通过传统和深度学习方法在提高准确性方面取得了显著的成功。
Designed an NLP pipeline for Kaggle-sourced SMS data, achieving 98% accuracy with machine learning and 97% with deep learning. Implemented advanced text preprocessing, showcasing expertise in NLP and machine learning. Notable success in accuracy improvement through traditional and deep learning methods. (2023-12-30, Jupyter Notebook, 0KB, 下载0次)
开发了一个强化学习人工反馈（RLHF）模型，以解决与印度尼西亚航运电子商务产品相关的挑战，情感分析的验证准确率达到97.79%，航运问题分类的验证准确度达到96.90%，RLHF模型本身的验证正确率达到83.33%。
Developed a Reinforcement Learning Human Feedback (RLHF) model to address the challenges associated with shipping e-commerce products in Indonesia, achieving a validation accuracy of 97.79% for sentiment analysis, 96.90% for shipping problem classification, and 83.33% for the RLHF model itself. (2023-12-28, Jupyter Notebook, 0KB, 下载0次)
用于预测给定查询的基于Django的Web应用程序属于哪种语言，即R、java、javascript、PHP...
A Django Based Web App for predicting a given query belongs to which language i.e R , java , javascript , PHP , python . The model is trained using LSTM s with a training accuracy of 97% and testing accuracy of 80%. The data that the model is trained with queries collected from a dataset in Kaggle originally extracted from StackOverflow. (2020-09-19, Jupyter Notebook, 23356KB, 下载0次)
C5_W4_A1_transformer_子类_v1（1）。学习目标创建位置编码以捕获顺序关系...
C5_W4_A1_Transformer_Subclass_v1 (1). Learning Objectives Create positional encodings to capture sequential relationships in data Calculate scaled dot- product self-attention with word embeddings Implement masked multi-head attention Build and train a Transformer model Fine-tune a pre-trained transformer model for Named Entity Recognition (2021-11-10, Jupyter Notebook, 41KB, 下载0次)
NLP_wuenda,吴恩达老师在2020年6月份推出了NLP课程,网址如下:[https: www.deeplearning.ai natural-language- processing- specialization 。本人忙里偷闲将老师的视频和作业都完...](https: www.deeplearning.ai natural- language-processing- specialization %E3%80%82%E6%9C%AC%E4%BA%BA%E5%BF%99%E9%87%8C%E5%81%B7%E9%97%B2%E5%B0%86%E8%80%81%E5%B8%88%E7%9A%84%E8%A7%86%E9%A2%91%E5%92%8C%E4%BD%9C%E4%B8%9A%E9%83%BD%E5%AE%8C%E6%88%90%E4%BA%86%EF%BC%8C%E5%90%8E%E7%BB%AD%E4%BC%9A%E6%8C%81%E7%BB%AD%E6%9B%B4%E6%96%B0%E8%AF%BE%E7%A8%8B%E7%9A%84%E8%B5%84%E6%96%99%E5%92%8C%E4%BD%9C%E4%B8%9A%E3%80%82)
NLP_ Wuenda, teacher Andrew Y. Ng launched the NLP course in June 2020, The website is as follows: [https: www.deeplearning. ai natural language - processing - specialization. I took a break from my busy schedule to finish the teacher s videos and assignments...] (https: www.deeplearning.ai natural- language-processing- specialization %E3%80%82%E6%9C%AC%E4%BA%BA%E5%BF%99%E9%87%8C%E5%81%B7%E9%97%B2%E5%B0%86%E8%80%81%E5%B8%88%E7%9A%84%E8%A7%86%E9%A2%91%E5%92%8C%E4%BD%9C%E4%B8%9A%E9%83%BD%E5%AE%8C%E6%88%90%E4%BA%86%EF%BC%8C%E5%90%8E%E7%BB%AD%E4%BC%9A%E6%8C%81%E7%BB%AD%E6%9B%B4%E6%96%B0%E8%AF%BE%E7%A8%8B%E7%9A%84%E8%B5%84%E6%96%99%E5%92%8C%E4%BD%9C%E4%B8%9A% E3%80%82) (2021-01-08, Jupyter Notebook, 399552KB, 下载0次)