机器学习
基于LSTM深度学习的磁盘阵列故障预测系统
本项目基于RGF和迁移学习构建了S.M.A.R.T磁盘故障预测系统，通过分析硬盘属性实现故障预测，提升数据安全性。RGF算法有效解决了GBDT的过拟合问题，而迁移学习方法提高了模型在不同磁盘模型间的适应性，降低了训练开销。该系统在云存储需求增加的背景下，能够有效预知风险并优化负载配置，具有重要的市场意义和应用价值。
数据挖掘十大算法详解
文章详细介绍了数据挖掘的十大算法，包括决策树C4.5、K-均值聚类、SVM、Apriori算法、EM算法、PageRank、Adaboost算法、K近邻算法、朴素贝叶斯分类器和CART。还提供了相关学习资源和图解。
TCN Hard Disk Failure Prediction
This document provides an overview of the TCN Hard Disk Failure Prediction project. It includes information on the SMART attributes selected for the prediction task, the code process for classification using Random Forest, TCN, and LSTM networks, as well as subflowcharts for feature selection, dataset partitioning, and training/testing processes for TCN and LSTM models.
Introduction of Hard Disk Failure Prediction using Machine Learning Method
This document presents a study on predicting hard disk drive failure using machine learning methods. The dataset used is from Backblaze, which includes S.M.A.R.T statistics and other attributes of hard drives. The document discusses data cleaning, feature selection, supervised learning techniques (Random Forest and XGBoost), unsupervised learning techniques (clustering and anomaly detection), and the results obtained. The study concludes that supervised learning techniques performed better in predicting hard disk failure compared to unsupervised learning techniques. Future work includes exploring a more generic approach to hard disk failure prediction and developing a dataset with critical features reported by all manufacturers.
Keras中文官方文档目录
本文整理了Keras中文官方文档的目录，包括视频教程、官方文档链接、常用模型API、层、数据预处理、损失函数、优化器、激活函数、回调函数、常用数据集、预训练模型、后端、初始化器、正则化器、可视化、Scikit-Learn API封装器及Keras模型部署等内容，提供了丰富的资源和工具以支持机器学习的学习和应用。
python工程题
本文是关于Python工程题的博客文章，涵盖了多个问题和解答，包括标准数据类型、字典的创建、双下划线和单下划线的区别、自省、文件遍历、迭代器和生成器的区别、*args和**kwargs、装饰器的使用、新式类和旧式类的区别、__new__和__init__的区别、单例模式的实现方式、作用域的类型、深拷贝和浅拷贝的区别、多线程和多进程的区别、is和==的区别、read、readline和readlines的区别、闭包、垃圾回收机制、+和join的区别、Lambda函数的使用、协程的理解、Python的GIL、字典的删除和合并、Python标准库、字典和JSON字符串的转化、列表去重的方法、Python2和Python3的区别、魔法方法的介绍、异常的解释、sort函数的实现原理、提高Python运行效率的方法、处理bug的方法、常用Linux命令等。