HM程序员-2025年python人工智能开发 V6.0【VIP】【配套课件资料更新】
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——/黑马程序员-2025年python人工智能开发 V6.0/
├──000 配套课件
| ├──002 Python进阶-V5.0-AI版
| | └──002 Python进阶-V5.0-AI版
| ├──004 机器学习-V5.0-AI版
| | ├──00-代码练习库
| | ├──day01-机器学习概述
| | ├──day02-KNN算法
| | ├──day03-线性回归
| | ├──day04-线性回归+逻辑回归
| | ├──day05-逻辑回归
| | ├──day06-决策树
| | ├──day07-集成学习
| | ├──day08-朴素贝叶斯和特征降维+聚类K-means
| | ├──day09-聚类kmeans算法+SVM
| | ├──day10-总结+拓展
| | ├──补充资料
| | ├──AI20期-大模型时代 .pdf2.90M
| | ├──api.xmind228.11kb
| | ├──机器学习V5.0课程逻辑图总结.xmind7.89M
| | └──机器学习V5.0课程总结.xmind12.25M
| ├──010 多模态大模型项目-V5.0-AI版
| | └──010 多模态大模型项目-V5.0-AI版
| ├──赠送往期多项目
| | ├──AI智慧交通项目实战.zip555.30M
| | ├──计算机视觉项目实战.zip372.32M
| | ├──投满分项目项目实战.zip8.18G
| | └──亿图人脸支付项目实战.zip12.49G
| ├──001 Python基础-V5.0-AI版.zip17.29G
| ├──002 Python进阶-V5.0-AI版.zip572.90M
| ├──003 数据处理和统计分析-V5.0-AI版.zip9.31G
| ├──004 机器学习-V5.0-AI版.zip290.08M
| ├──005 金融风控-V5.0-AI版.zip792.93M
| ├──006 深度学习基础-V5.0-AI版.zip344.29M
| ├──007 NLP自然语言处理+GPT-V5.0-AI版.zip9.39G
| ├──008 知识图谱-V5.0-AI版.zip1.89G
| ├──009 大模型开发基础与项目-V5.0-AI版.zip26.95G
| └──010 多模态大模型项目-V5.0-AI版.zip146.88M
├──01_Python基础-V6.X版-14天-AI版
| ├──01.Linux基础
| | ├──Day01_Linux基础
| | └──Day02_Linux基础
| ├──02.MySQL基础
| | ├──day01-Mysql基础
| | ├──day02-Mysql查询
| | ├──day03-Mysql多表查询
| | └──day04-窗口函数+练习题
| └──03.Python基础
| | ├──day01
| | ├──day02
| | ├──day03
| | ├──day04
| | ├──day05
| | ├──day06
| | ├──day07
| | └──day08
├──02_Python进阶-V6.X版-9天-AI版
| ├──day01-面向对象基础
| | ├──01.Python进阶-大纲介绍_.wmv18.59M
| | ├──02.今日内容大纲介绍_.wmv11.81M
| | ├──03.面向对象-初识_.wmv69.53M
| | ├──04.面向对象-三大特征介绍_.wmv83.53M
| | ├──05.定义类和对象_.wmv82.56M
| | ├──06.self对象介绍_.wmv55.28M
| | ├──07.在类内部调用类的函数_.wmv40.38M
| | ├──08.在类外定义和获取属性_.wmv46.06M
| | ├──09.上午内容回顾_.wmv74.16M
| | ├──10.在类内获取属性_.wmv35.72M
| | ├──11.魔法方法-init-无参数_.wmv95.84M
| | ├──12.魔法方法-init-有参数_.wmv83.41M
| | ├──13.魔法方法-str和del_.wmv74.16M
| | ├──14.案例-减肥_.wmv72.50M
| | └──15.案例-烤地瓜_.wmv128.91M
| ├──day02-面向对象高级
| | ├──01.昨日反馈处理_.wmv77.19M
| | ├──02.今日内容大纲介绍_.wmv37.88M
| | ├──03.定义类的格式_.wmv19.09M
| | ├──04.继承-入门_.wmv56.91M
| | ├──05.继承-单继承_.wmv34.09M
| | ├──06.继承-多继承_.wmv68.47M
| | ├──07.继承-方法重写-入门_.wmv48.91M
| | ├──08.继承-子类访问父类成员-方式1_.wmv193.62M
| | ├──09.继承-子类访问父类成员-方式2_.wmv105.72M
| | ├──10.继承-多层继承_.wmv46.53M
| | ├──11.封装-私有化属性_.wmv90.66M
| | ├──12.上午内容回顾_.wmv60.53M
| | ├──13.封装-私有化方法_.wmv85.88M
| | ├──14.扩展-方法重写案例-手机类_.wmv77.72M
| | ├──15.多态-入门_.wmv110.41M
| | ├──16.扩展_Java版多态_.wmv110.28M
| | ├──17.多态案例_战斗机_.wmv97.97M
| | ├──18.抽象类详解_.wmv55.22M
| | ├──19.类属性和对象属性详解_.wmv100.41M
| | └──20.类方法和静态方法详解_.wmv95.81M
| ├──day03-学生管理系统-深拷贝浅拷贝
| | ├──01.昨日反馈和作业处理_.wmv163.03M
| | ├──02.面向对象版-学生管理系统-需求分析_.wmv73.16M
| | ├──03.学生类-代码实现_.wmv60.75M
| | ├──04.学生管理系统文件-搭建提示界面_.wmv47.91M
| | ├──05.学生管理系统文件-搭建基本框架_.wmv55.94M
| | ├──06.学生管理系统文件-业务逻辑代码实现_.wmv103.59M
| | ├──07.main模块-搭建程序的主入口_.wmv42.78M
| | ├──08.学生管理系统文件-添加学生_.wmv61.94M
| | ├──09.学生管理系统文件-查看所有学生信息_.wmv41.16M
| | ├──10.学生管理系统文件-修改学生信息_.wmv84.56M
| | ├──11.学生管理系统文件-删除学生信息_.wmv40.44M
| | ├──12.学生管理系统文件-查询单个学生信息_.wmv22.62M
| | ├──13.上午内容回顾_.wmv27.72M
| | ├──14.扩展_dict属性_.wmv57.78M
| | ├──15.扩展_with_open语法_.wmv24.34M
| | ├──16.学生管理系统文件-保存学生信息_.wmv54.97M
| | ├──17.学生管理系统文件-加载学生信息_.wmv137.91M
| | ├──18.扩展-把show_view()函数定义成静态方法_.wmv53.50M
| | ├──19.深浅拷贝-普通赋值_.wmv90.03M
| | ├──20.深浅拷贝-浅拷贝_.wmv90.50M
| | ├──21.深浅拷贝-深拷贝_.wmv116.19M
| | └──22.今日内容总结_.wmv39.38M
| ├──day04_闭包和装饰器
| | ├──01.昨日内容回顾_.wmv58.31M
| | ├──02.函数名-作为对象_.wmv77.84M
| | ├──03.函数名-作为实参传递_.wmv43.81M
| | ├──04.闭包-入门_.wmv136.59M
| | ├──05.闭包-图解_.wmv46.19M
| | ├──06.闭包-nonlocal关键字_.wmv72.66M
| | ├──07.装饰器-入门_.wmv98.06M
| | ├──08.装饰器-无参无返回值_.wmv58.97M
| | ├──09.装饰器-有参无返回值_.wmv29.66M
| | ├──10.上午内容回顾_.wmv35.81M
| | ├──11.装饰器-无参有返回值_.wmv55.28M
| | ├──12.装饰器-有参有返回值_.wmv29.91M
| | ├──13.装饰器-可变参数_.wmv41.78M
| | ├──14.多个装饰器-装饰1个函数_.wmv55.94M
| | ├──15.多个装饰器-执行流程_.wmv73.16M
| | ├──16.带有参数的装饰器(上)_.wmv133.09M
| | └──17.带有参数的装饰器(下)_.wmv41.84M
| ├──day05_网编和多线程
| | ├──01.昨日反馈处理_.wmv65.81M
| | ├──02.今日内容大纲介绍_.wmv11.00M
| | ├──03.网络编程-介绍_.wmv94.78M
| | ├──04.端口号-介绍_.wmv71.25M
| | ├──05.协议-介绍_.wmv69.16M
| | ├──06.网络通信-原理_.wmv41.22M
| | ├──07.socket-入门_.wmv28.62M
| | ├──08.TCP流程分析_.wmv60.81M
| | ├──09.字符串和二进制数据相互转换_.wmv79.91M
| | ├──10.案例-收发1句话-服务器端代码实现_.wmv88.88M
| | ├──11.案例-收发1句话-客户端代码实现_.wmv87.50M
| | ├──12.上午内容回顾_.wmv70.66M
| | ├──13.服务器端-端口号复用_.wmv62.81M
| | ├──14扩展_.服务器端-接收多客户端消息_.wmv130.97M
| | ├──15.扩展_长连接_.wmv88.84M
| | ├──16.扩展_文件上传_.wmv136.44M
| | ├──17.扩展_文件上传_支持多客户端_.wmv66.94M
| | ├──18.并行和并发介绍_.wmv102.28M
| | ├──19.多任务介绍_.wmv92.78M
| | └──20.多进程-代码实现_.wmv149.06M
| ├──day06_多线程_生成器
| | ├──01.昨日反馈处理_.wmv43.84M
| | ├──02.今日内容大纲介绍_.wmv20.59M
| | ├──03.多进程-参数解释_.wmv83.03M
| | ├──04.多进程-获取进程编号_.wmv96.19M
| | ├──05.多进程-进程之间数据相互隔离_.wmv75.97M
| | ├──06.默认-主进程会等待子进程结束再结束_.wmv43.00M
| | ├──07.设置主进程结束-子进程同步结束_.wmv75.50M
| | ├──08.多线程-基本概述_.wmv43.81M
| | ├──09.多线程-入门_.wmv29.78M
| | ├──10.多线程-参数解释_.wmv41.59M
| | ├──11.多线程-执行顺序_.wmv72.38M
| | ├──12.多线程-守护线程_.wmv26.81M
| | ├──13.上午内容回顾_.wmv53.09M
| | ├──14.多线程-线程间共享数据_.wmv47.81M
| | ├──15.多线程-操作共享变量-出现非法值_.wmv97.47M
| | ├──16.多线程-解决线程安全问题_.wmv82.00M
| | ├──17.多线程和多进程的区别_.wmv30.88M
| | ├──18.回顾-with open语法_.wmv51.19M
| | └──19.自定义代码实现-上下文管理器(了解)_.wmv144.91M
| ├──day07_正则表达式
| | ├──01.昨日反馈处理_.wmv54.22M
| | ├──02.今日内容大纲介绍_.wmv15.53M
| | ├──03.生成器-推导式写法_.wmv55.69M
| | ├──04.生成器-yield关键字_.wmv53.59M
| | ├──05.生成器-自定义数据迭代器_.wmv212.59M
| | ├──06.property-装饰器用法_.wmv69.62M
| | ├──07.property-修饰类变量_.wmv45.19M
| | ├──08.正则表达式-相关概述_.wmv49.81M
| | ├──09.正则表达式-入门_.wmv67.03M
| | ├──10.正则表达式-数量词_.wmv55.81M
| | ├──11.正则表达式-替换_.wmv71.09M
| | ├──12.上午内容回顾_.wmv45.41M
| | ├──13.正则表达式-校验单个字符_.wmv102.94M
| | ├──14.正则表达式-校验多个字符_.wmv76.78M
| | ├──15.正则表达式-校验开头和结尾_.wmv78.62M
| | ├──16.正则表达式-或者 和 分组_.wmv108.25M
| | ├──17.正则表达式-分组详解_.wmv164.84M
| | ├──18.数据结构和算法-入门_.wmv30.78M
| | ├──19.数据结构和算法-特点_.wmv74.34M
| | └──20.时间复杂度简介_.wmv81.28M
| ├──day08_算法和数据结构
| | ├──01.昨日反馈处理_.wmv78.09M
| | ├──02.今日内容大纲介绍_.wmv25.47M
| | ├──03.时间复杂度介绍_.wmv94.06M
| | ├──04.时间复杂度计算_.wmv61.41M
| | ├──05.最优最坏时间复杂度介绍_.wmv32.03M
| | ├──06.常见的时间复杂度介绍(掌握)_.wmv53.06M
| | ├──07.空间复杂度介绍(了解)_.wmv28.00M
| | ├──08.回顾-数据结构和算法概述_.wmv23.50M
| | ├──09.数据结构-划分_.wmv43.66M
| | ├──10.数据结构-线性结构-顺序表介绍_.wmv59.44M
| | ├──11.数据结构-线性结构-顺序表扩容策略_.wmv50.31M
| | ├──12.数据结构-线性结构-顺序表-添加和删除_.wmv64.34M
| | ├──13.上午内容回顾_.wmv49.91M
| | ├──14.链表-介绍_.wmv117.44M
| | ├──15.自定义代码模拟链表-创建节点类_.wmv64.97M
| | ├──16.链表类-架构搭建_.wmv78.88M
| | ├──17.链表类-判断链表是否为空_.wmv40.06M
| | ├──18.链表类-获取长度_.wmv27.91M
| | ├──19.链表类-遍历链表_.wmv31.41M
| | ├──20.链表类-往头部添加元素_.wmv33.19M
| | ├──21.链表类-往末尾添加元素_.wmv36.53M
| | ├──22.链表类-往中间添加元素_.wmv108.38M
| | ├──23.链表类-删除元素_.wmv80.00M
| | ├──24.链表类-查找元素_.wmv52.69M
| | └──25.顺序表和链表区别_.wmv36.59M
| └──day09_算法和数据结构
| | ├──01.昨日内容回顾_.wmv93.94M
| | ├──02.排序算法-稳定性介绍_.wmv26.25M
| | ├──03.冒泡排序-原理介绍_.wmv72.09M
| | ├──04.冒泡排序-代码实现_.wmv66.03M
| | ├──05.选择排序-分析流程_.wmv53.88M
| | ├──06.选择排序-代码实现_.wmv62.78M
| | ├──07.选择排序-代码实现_.wmv119.16M
| | ├──08.快速排序-思路分析_.wmv60.50M
| | ├──09.快速排序-代码实现_.wmv144.66M
| | ├──10.快速排序-图解_.wmv86.28M
| | ├──11.上午内容回顾_.wmv33.09M
| | ├──12.二分查找-递归版_.wmv53.78M
| | ├──13.二分查找-非递归版_.wmv49.31M
| | ├──14.数据结构-树-相关概述_.wmv70.72M
| | ├──15.数据结构-树-应用场景和存储_.wmv72.62M
| | ├──16.树形结构-应用场景-简介_.wmv16.44M
| | ├──17.树形结构-相关公式_.wmv27.59M
| | ├──18.自定义代码-模拟树形结构-添加元素_.wmv196.06M
| | ├──19.模拟树形结构-遍历-广度优先_.wmv72.22M
| | ├──20.模拟树形结构-遍历-深度优先_.wmv126.84M
| | └──21.根据遍历结果-逆推树形结构_.wmv29.34M
├──03-数据处理和统计分析-V6.X版-10天-AI版
| ├──day01
| | ├──01.阶段大纲介绍_.wmv14.41M
| | ├──02.今日内容大纲介绍_.wmv28.81M
| | ├──03.计算机介绍_.wmv95.25M
| | ├──04.Linux系统介绍_.wmv68.72M
| | ├──05.虚拟化软件介绍_.wmv77.41M
| | ├──06.Vmware-软件安装_.wmv44.12M
| | ├──07.手动安装虚拟机(了解即可)_.wmv89.19M
| | ├──08.挂载虚拟机到Vmware软件(掌握)_.wmv32.03M
| | ├──09.Linux-快照_.wmv27.12M
| | ├──10.FinalShell-连接Linux虚拟机_.wmv126.38M
| | ├──11.Linux-命令格式介绍_.wmv19.94M
| | ├──11.Linux目录-介绍_.wmv79.38M
| | ├──12.上午内容回顾_.wmv36.69M
| | ├──13.Linux基础命令-ls命令_.wmv44.19M
| | ├──14.Linux基础命令-cd和pwd_.wmv39.22M
| | ├──15.Linux基础命令-创建目录_.wmv17.81M
| | ├──16.Linux基础命令-文件操作(上)_.wmv91.22M
| | ├──17.Linux基础命令-文件操作(下)_.wmv54.81M
| | ├──18.Linux基础命令-查找命令_.wmv76.56M
| | ├──19.Linux基础命令-grep和管道命令_.wmv51.03M
| | ├──20.Linux基础命令-echo, 重定向, tail命令_.wmv55.47M
| | ├──21.Linux基础命令-vi命令入门_.wmv29.03M
| | ├──22.Linux基础命令-vi常用快捷键_.wmv72.44M
| | └──23.扩展_man命令 和 help选项_.wmv28.44M
| ├──day02
| | ├──扩展
| | ├──01.昨日反馈处理_.wmv55.50M
| | ├──02.今日内容大纲介绍_.wmv6.88M
| | ├──03.root用户-初识_.wmv73.44M
| | ├──04.用户和用户组相关操作_.wmv76.84M
| | ├──05.权限查看相关_.wmv56.25M
| | ├──06.权限管理命令-chmod_.wmv61.97M
| | ├──07.权限管理命令-chown_.wmv28.84M
| | ├──08.Linux-常用快捷键_.wmv19.97M
| | ├──09.Linux-yum方式安装软件_.wmv59.19M
| | ├──10.Linux-服务管理命令-systemctl_.wmv34.09M
| | ├──11.Linux-软连接_.wmv42.78M
| | ├──12.Linux-硬链接_.wmv22.59M
| | ├──13.上午内容回顾_.wmv14.41M
| | ├──14.IP地址-介绍_.wmv21.59M
| | ├──15.网络相关-配置域名映射_.wmv78.03M
| | ├──16.网络传输-下载和发起网络请求_.wmv117.12M
| | ├──17.网络相关-端口号相关操作_.wmv23.41M
| | ├──18.进程相关_.wmv28.91M
| | ├──19.环境变量相关_.wmv60.31M
| | ├──20.Linux-上传和下载_.wmv44.53M
| | ├──21.压缩和解压缩-tarball 归档方式_.wmv56.78M
| | └──22.压缩和解压缩-zip方式_.wmv16.34M
| ├──day03
| | ├──01.昨日内容回顾_.wmv12.81M
| | ├──02.今日内容大纲介绍_.wmv8.19M
| | ├──03.数据库简介_.wmv27.31M
| | ├──04.数据库分类_.wmv39.47M
| | ├──05.MySQL版本介绍_.wmv45.66M
| | ├──06.MySQL-安装_.wmv82.81M
| | ├──07.MySQL-安装时可能遇到的问题_.wmv52.44M
| | ├──08.MySQL-登陆和登出_.wmv64.69M
| | ├──09.DataGrip连接MySQL_.wmv97.03M
| | ├──10.扩展_PyCharm连接MySQL_.wmv21.31M
| | ├──11.扩展_DataGrip基本设置_.wmv22.53M
| | ├──12.SQL语句-分类_.wmv35.38M
| | ├──13.SQL语句-通用语法和常用数据类型_.wmv71.91M
| | ├──14.上午内容回顾_.wmv60.75M
| | ├──15.DDL-操作数据库_.wmv91.53M
| | ├──16.DDL-操作数据表_.wmv60.81M
| | ├──17.DDL-操作字段_.wmv64.84M
| | ├──18.DML-添加表数据_.wmv76.94M
| | ├──19.DML-修改和删除表数据_.wmv80.84M
| | ├──20.delete from 和 truncate table区别_.wmv64.22M
| | ├──21.扩展_如何备份数据表_.wmv43.31M
| | └──22.约束详解_.wmv78.50M
| ├──day04
| | ├──01.昨日内容回顾及反馈处理_.wmv37.62M
| | ├──02.单表查询-简单查询_.wmv121.19M
| | ├──03.单表查询-条件查询_.wmv98.88M
| | ├──04.单表查询-排序查询_.wmv28.12M
| | ├──05.单表查询-聚合查询_.wmv104.44M
| | ├──06.单表查询-分组查询_.wmv144.22M
| | ├──07.单表查询-分页查询_.wmv113.47M
| | ├──08.上午内容回顾_.wmv44.78M
| | ├──09.多表建表-一对多_.wmv137.97M
| | ├──10.扩展_多表建表-多对多_.wmv77.59M
| | ├──11.扩展_多表建表-一对一_.wmv15.69M
| | ├──12.多表查询-交叉查询_.wmv47.34M
| | ├──13.多表查询-连接查询_.wmv58.09M
| | ├──14.多表查询-子查询_.wmv71.81M
| | ├──15.多表查询-自关联查询_.wmv164.94M
| | └──16.窗口函数入门_.wmv121.72M
| ├──day05
| | ├──01.昨日反馈处理_.wmv32.44M
| | ├──02.今日内容大纲介绍_.wmv34.06M
| | ├──03.Python数据分析的优势_.wmv20.91M
| | ├──04.Python-常见的开源库_.wmv39.28M
| | ├──05.Anaconda-环境搭建_.wmv68.16M
| | ├──06.Anaconda-如何安装第三方资源库_.wmv53.38M
| | ├──07.Anaconda-沙箱相关操作_.wmv58.81M
| | ├──08.Jupyter Notebook-入门_.wmv97.44M
| | ├──09.Jupyter Notebook-常用快捷键_.wmv46.59M
| | ├──10.扩展_Jupyter lab演示_.wmv28.94M
| | ├──11.PyCharm集成Anaconda-jupyter Notebook_.wmv127.97M
| | ├──12.上午内容回顾_.wmv111.97M
| | ├──13.Numpy-常用属性_.wmv88.28M
| | ├──14.创建ndarray_数组_zeros_ones_empty_.wmv115.12M
| | ├──15.创建ndarray数组_arange_matrix_.wmv49.00M
| | ├──16.创建ndarray数组_rand_randint_uniform_.wmv31.16M
| | ├──17.创建ndarray数组_astype函数_.wmv27.62M
| | ├──18.创建ndarray数组_logspace等比数列和linspace等差数列_.wmv39.03M
| | ├──19.ndarray内置函数-基本函数_.wmv80.69M
| | ├──20.ndarray内置函数-统计函数_.wmv47.50M
| | ├──21.ndarray内置函数_比较-排序-去重_.wmv71.75M
| | └──22.ndarray_运算_.wmv90.22M
| ├──day06
| | ├──01.昨日反馈处理_.wmv36.03M
| | ├──02.今日内容大纲介绍_.wmv10.06M
| | ├──03.Panda-数据结构介绍_.wmv65.47M
| | ├──04.通过列表创建Series对象_.wmv82.22M
| | ├──05.扩展_通过元组_字典创建Series对象_.wmv11.31M
| | ├──06.创建DataFrame对象_.wmv83.28M
| | ├──07.Series对象常用属性_.wmv160.28M
| | ├──08.Series对象常用函数_.wmv83.38M
| | ├──09.Series案例-电影数据_.wmv114.09M
| | ├──10.Series-结合布尔值操作_.wmv89.53M
| | ├──11.Series-计算_.wmv59.97M
| | ├──12.上午内容回顾_.wmv52.59M
| | ├──13.DataFrame-常用属性介绍_.wmv35.28M
| | ├──14.DataFrame-常用函数介绍_.wmv88.41M
| | ├──15.DataFrame-布尔索引_.wmv30.88M
| | ├──16.DataFrame-计算_.wmv47.09M
| | ├──17.DataFrame-索引操作-入门_.wmv114.94M
| | ├──18.DataFrame-修改行索引和列名_.wmv126.34M
| | ├──19.DataFrame-添加-删除-插入列_.wmv113.44M
| | └──20.DataFrame-导入和导出数据_.wmv198.38M
| ├──day07
| | ├──01.昨日内容回顾_.wmv20.44M
| | ├──02.今日内容大纲介绍_.wmv9.41M
| | ├──03.DataFrame-加载行, 列数据_.wmv125.00M
| | ├──04.DataFrame-加载指定行列的数据_.wmv59.78M
| | ├──05.DataFrame-聚合统计_.wmv77.78M
| | ├──06.DataFrame-基本绘图_.wmv15.66M
| | ├──07.DataFrame-常用的统计值的方法_.wmv36.47M
| | ├──08.DataFrame-常用排序方法_.wmv81.59M
| | ├──09.链家案例-前5个需求_.wmv192.75M
| | ├──10.链家案例-6~8需求_.wmv121.56M
| | ├──11.链家案例-9~12需求_.wmv87.72M
| | ├──12.上午内容回顾_.wmv27.66M
| | ├──13.数据组合-concat()函数_.wmv118.12M
| | ├──14.数据组合-append()函数_.wmv81.44M
| | ├──15.数据组合-merge-一对一_.wmv170.72M
| | ├──16.数据组合-merge-多对一_.wmv90.81M
| | ├──17.数组聚合-join方式(了解)_.wmv97.69M
| | ├──18.缺失值-初识_.wmv45.03M
| | ├──19.缺失值-加载_.wmv35.03M
| | ├──20.缺失值-查看_.wmv74.34M
| | ├──21.缺失值处理-删除_.wmv38.19M
| | ├──22.缺失值处理-非线性填充_.wmv30.81M
| | └──23.缺失值处理-线性填充_.wmv94.59M
| ├──day08
| | ├──01.昨日反馈处理及内容回顾_.wmv48.75M
| | ├──02.今日内容大纲介绍_.wmv17.44M
| | ├──03.apply函数-操作Series对象_.wmv52.53M
| | ├──04.apply函数-操作df对象_.wmv68.78M
| | ├──05.apply函数案例-计算某列的缺失值占比_.wmv83.62M
| | ├──06.向量化函数介绍_.wmv45.47M
| | ├──07.apply函数-结合lambda表达式使用_.wmv40.31M
| | ├──08.分组聚合演示_.wmv80.34M
| | ├──09.分组转换演示_.wmv158.00M
| | ├──10.分组过滤演示_.wmv19.75M
| | ├──11.GroupBy分组对象介绍_.wmv57.53M
| | ├──12.上午内容回顾_.wmv38.75M
| | ├──13.零售会员数据分析-需求介绍_.wmv58.06M
| | ├──14.零售会员数据分析-月增量实现及可视化_.wmv78.69M
| | ├──15.零售会员数据分析-透视表方式计算月增量_.wmv22.88M
| | ├──16.零售会员数据分析-计算月存量_.wmv64.56M
| | ├──17.零售会员数据分析-会员增量等级分布_.wmv127.38M
| | ├──18.回顾_python中的日期类型_.wmv20.22M
| | ├──19.Pandas中的日期类型介绍_.wmv59.81M
| | ├──20.提取日期的各个部分_.wmv51.47M
| | ├──21.日期运算_.wmv118.62M
| | ├──22.获取连续的日期_.wmv46.38M
| | ├──23.Python可视化组件介绍_.wmv18.19M
| | ├──24.Matplotlib-状态接口方式绘图_.wmv53.69M
| | └──25.Matplotlib-面向对象方式绘图_.wmv24.66M
| ├──day09
| | ├──01.今日内容大纲介绍_.wmv135.09M
| | ├──02.anscombe数据集可视化_.wmv118.69M
| | ├──03.MatPlotlib-单变量-直方图_.wmv60.59M
| | ├──04.MatPlotlib-双变量-散方图_.wmv34.16M
| | ├──05.MatPlotlib-多变量-散点图_.wmv89.06M
| | ├──06.Pandas-单变量-柱状图(条形图)_.wmv71.66M
| | ├──07.Pandas-单变量-折线图-面积图-饼图_.wmv61.31M
| | ├──08.Seaborn-单变量-直方图_.wmv70.44M
| | ├──09.Seaborn-单变量-密度图_.wmv31.91M
| | ├──10.Seaborn-单变量-计数图_.wmv21.06M
| | ├──11.Seaborn-双变量-散点图_.wmv129.59M
| | ├──12.上午内容回顾_.wmv16.53M
| | ├──13.Seaborn-双变量-2D密度图_.wmv40.34M
| | ├──14.Seaborn-双变量-箱线图_.wmv75.69M
| | ├──15.Seaborn-双变量-小提琴图_.wmv54.44M
| | └──16.Seaborn-样式介绍_.wmv55.41M
| └──day10
| | ├──01.昨日内容回顾_.wmv67.88M
| | ├──02.会员价值度预估模型介绍_.wmv51.66M
| | ├──03.RFM案例-基本实现过程介绍_.wmv38.38M
| | ├──04.RFM案例-背景介绍_.wmv22.62M
| | ├──05.RFM案例-数据源介绍_.wmv20.41M
| | ├──06.RFM案例-加载数据及查看格式_.wmv123.31M
| | ├──07.RFM案例-数据预处理_.wmv161.59M
| | ├──08.RFM案例-汇总数据_.wmv45.44M
| | ├──09.RFM案例-计算RFM各项指标值_.wmv221.25M
| | ├──10.上午内容回顾_.wmv97.72M
| | ├──11.RFM案例-计算最终结果_.wmv39.00M
| | ├──12.RFM案例-绘制3D柱状图_.wmv91.50M
| | ├──13.RFM案例-导出结果到本地文件或者数据库_.wmv110.78M
| | ├──14.RFM案例-总结及细节_.wmv47.84M
| | ├──15.RFM案例-面向对象版_.wmv226.28M
| | ├──16.扩展_迭代器_.wmv107.97M
| | ├──17.总结_Linux_MySQL_.wmv60.12M
| | └──18.总结_Numpy_Pandas_.wmv112.47M
├──04_机器学习-V6.X版-10天-AI版
| ├──day01-机器学习概述
| | ├──01-课前内容_.wmv94.94M
| | ├──02-人工智能三大概念_.wmv106.56M
| | ├──03-应用领域及发展史_.wmv56.16M
| | ├──04-常用术语（特征，样本，标签）_.wmv28.38M
| | ├──05-机器学习算法分类_.wmv74.72M
| | ├──06-建模流程+线性回归案例demo_.wmv119.38M
| | ├──07-Anaconda+Pycharm_.wmv81.41M
| | ├──08-线性回归-模型保存_.wmv47.41M
| | ├──09-KNN案例_.wmv94.12M
| | ├──10-kmeans案例_.wmv170.78M
| | ├──11-特征工程_.wmv40.41M
| | ├──12-过拟合欠拟合_.wmv34.81M
| | ├──13-sklearn库_.wmv15.25M
| | └──14-今日总结_.wmv65.34M
| ├──day02-KNN算法
| | ├──01-昨日回顾_.wmv129.38M
| | ├──02-KNN思想_.wmv20.09M
| | ├──03-KNN思想2_.wmv61.41M
| | ├──04-KNN-API_.wmv36.31M
| | ├──05-距离度量_.wmv42.91M
| | ├──06-模型预处理-归一化_.wmv61.75M
| | ├──07-模型预处理-标准化及总结_.wmv64.50M
| | ├──08-模型预处理案例-鸢尾花-数据导入_.wmv54.69M
| | ├──09-鸢尾花特征数据展示_.wmv95.81M
| | ├──10-数据可视化+数据集切分_.wmv50.38M
| | ├──11-模型预测评估_.wmv126.25M
| | ├──12-交叉验证网格搜索_.wmv96.97M
| | ├──13-交叉验证代码解析_.wmv39.88M
| | └──14-今日总结_.wmv91.53M
| ├──day03-线性回归
| | ├──01-昨日回顾_.wmv100.56M
| | ├──02-KNN-MINIST-数据获取_.wmv135.69M
| | ├──03-KNN_MINIST-模型训练_.wmv132.59M
| | ├──04-KNN-作业_.wmv20.09M
| | ├──05-线性回归介绍_.wmv19.72M
| | ├──06-线性回归基本求解及概念介绍_.wmv86.94M
| | ├──07-导数和矩阵_.wmv138.09M
| | ├──08-练习题_.wmv28.03M
| | ├──09-正规方程法_.wmv49.03M
| | └──10-梯度下降法_.wmv20.81M
| ├──day04-线性回归+逻辑回归
| | ├──01-昨日回顾_.wmv148.53M
| | ├──02-梯度下降法案例_.wmv39.88M
| | ├──03-案例-银行信贷_.wmv105.59M
| | ├──04-梯度下降法算法分类_.wmv93.44M
| | ├──05-梯度下降算法总结_.wmv15.75M
| | ├──06-评估指标_.wmv43.44M
| | ├──07-梯度下降法+正规方程法API_.wmv32.88M
| | ├──08-波士顿房价预测-正规方程法_.wmv85.03M
| | ├──09-波士顿房价预测-梯度下降法_.wmv38.78M
| | ├──10-过拟合欠拟合实现_.wmv69.25M
| | ├──11-正则化-L1_.wmv86.53M
| | ├──12-正则化-L2正则化及代码实现_.wmv91.62M
| | ├──13-线性回归总结_.wmv130.16M
| | ├──14-逻辑回归基本介绍_.wmv63.97M
| | └──15-逻辑回归基本原理_.wmv66.00M
| ├──day05-逻辑回归
| | ├──01-总结回顾_.wmv197.84M
| | ├──02-逻辑回归案例-cancer预测_.wmv177.84M
| | ├──03-评估-混淆矩阵_.wmv78.62M
| | ├──04-评估-P_R_f1-score_.wmv69.81M
| | ├──05-ROC_AUC_.wmv102.12M
| | ├──06-AUC_API_.wmv46.47M
| | ├──06-案例-客户流失-数据预处理_.wmv209.38M
| | ├──07-案例-客户流失-模型训练_.wmv133.78M
| | └──08-今日总结_.wmv99.75M
| ├──day06-决策树
| | ├──01-昨日回顾_.wmv79.78M
| | ├──02-决策树思想_.wmv16.19M
| | ├──03-ID3决策树-信息增益_.wmv54.28M
| | ├──04-信息增益-详解_.wmv19.91M
| | ├──05-ID3决策树-总结_.wmv71.22M
| | ├──06-C4.5-信息增益率_.wmv34.47M
| | ├──07-C4.5-案例_.wmv34.84M
| | ├──08-C4.5总结_.wmv11.22M
| | ├──09-CART决策树-案例_.wmv63.50M
| | ├──10-CART决策树-案例+总结_.wmv47.47M
| | ├──11-泰坦尼克号案例-讲解_.wmv71.31M
| | ├──12-泰坦尼克号案例-代码实现_.wmv159.84M
| | ├──13-回归决策树-思想_.wmv54.19M
| | ├──14-回归决策树-案例_.wmv51.72M
| | ├──15-决策树剪枝_.wmv59.88M
| | └──16-今日总结_.wmv122.28M
| ├──day07-集成学习
| | ├──01-昨日回顾_.wmv104.66M
| | ├──02-集成学习思想_.wmv67.34M
| | ├──03-随机森林思想_.wmv118.72M
| | ├──04-Adaboost思想_.wmv202.62M
| | ├──04-随机森林案例_.wmv61.84M
| | ├──05-adaboost案例-葡萄酒分类 _.wmv112.25M
| | ├──06-GBDT思想_.wmv71.19M
| | ├──07-GBDT 案例-泰坦尼克号-代码实现_.wmv58.47M
| | ├──08-XGBoost原理_.wmv92.84M
| | └──09-XGBoost案例_.wmv189.72M
| ├──day08-朴素贝叶斯和特征降维+聚类K-means
| | ├──01-昨日回顾_.wmv174.78M
| | ├──02-朴素贝叶斯思想_.wmv107.91M
| | ├──03-总结_.wmv31.75M
| | ├──04-朴素贝叶斯情感分类案例_.wmv169.53M
| | ├──05-低方差过滤法_.wmv39.75M
| | ├──06-PCA_.wmv32.84M
| | ├──07-相关系数法mp4_.wmv95.78M
| | ├──08-特征降维总结_.wmv14.41M
| | ├──09-朴素贝叶斯总结_.wmv64.88M
| | ├──10-特征降维总结_.wmv37.66M
| | ├──11-聚类基本介绍_.wmv30.44M
| | ├──12-K-means API_.wmv40.03M
| | ├──13-K-means实现流程_.wmv41.38M
| | └──14-K-Means评估指标_.wmv145.78M
| ├──day09-聚类kmeans算法+SVM
| | ├──01-朴素贝叶斯+特征降维总结_.wmv96.53M
| | ├──02-K-means总结_.wmv66.38M
| | ├──03-案例-客户分析-数据展示_.wmv146.66M
| | ├──04-案例-客户分析-模型训练及结果分析_.wmv180.56M
| | ├──05-SVM思想_.wmv59.12M
| | ├──06-SVM案例-鸢尾花-数据读取处理_.wmv91.62M
| | ├──07-SVM案例-鸢尾花-模型训练展示_.wmv138.66M
| | ├──08-SVM-C值测试_.wmv25.88M
| | ├──09-SVM算法原理_.wmv106.91M
| | └──10-SVM核函数_.wmv74.94M
| ├──day10-总结+拓展
| | ├──01-总结回顾-1_.wmv194.34M
| | ├──02-总结回顾-2_.wmv177.69M
| | ├──03-总结回顾-3_.wmv176.44M
| | ├──04-总结回顾-4_.wmv135.78M
| | ├──05-大模型时代-1_.wmv194.50M
| | └──06-大模型时代-2_.wmv215.25M
| ├──每日算法_.wmv62.41M
| └──算法-毕天宇-滑动窗口法_.wmv129.56M
├──06_金融风控-V6.X版-8天-AI版
| ├──day01
| | ├──01【了解】-课程资料说明_.wmv14.09M
| | ├──02【了解】-项目整体介绍_.wmv23.03M
| | ├──03【了解】-今日内容介绍_.wmv24.06M
| | ├──04【理解】信贷&风控介绍_.wmv105.75M
| | ├──05【了解】消费贷和现金贷_.wmv139.78M
| | ├──06【了解】常见风险介绍_.wmv42.06M
| | ├──07【理解】风控术语名词_.wmv48.56M
| | ├──08【理解】风控业务案例-背景&需求说明_.wmv68.78M
| | ├──09【实现】风控业务案例-数据加载_.wmv74.75M
| | ├──10【实现】风控业务案例-数据处理_.wmv127.50M
| | ├──11【实现】风控业务案例-增加中间字段_.wmv135.00M
| | ├──12【实现】风控业务案例-季度转换和过滤_.wmv106.59M
| | ├──13【实现】风控业务案例-计算坏账率_.wmv120.34M
| | ├──14【实现】风控业务案例-就散入催率_.wmv143.84M
| | ├──15【实现】风控业务案例-回收账单数_.wmv86.12M
| | ├──16【小结】风控业务案例小结_.wmv39.12M
| | ├──17【理解】信贷业务如何运行_.wmv66.50M
| | ├──18【了解】业务转换和漏斗_.wmv34.75M
| | ├──19【了解】业务表说明_.wmv129.38M
| | ├──20【了解】前置操作（配置终端工具和datagrip）_.wmv65.34M
| | ├──21【了解】风控报表指标介绍_.wmv15.34M
| | └──22【了解】数据导入_.wmv30.31M
| ├──day02
| | ├──01【回顾】昨日内容回顾_.wmv148.00M
| | ├──02【了解】各阶段转化率表（1）_.wmv218.41M
| | ├──03【了解】各阶段转化率表（2）_.wmv216.25M
| | ├──04【了解】各阶段转换率表（3）_.wmv262.38M
| | ├──05【了解】各阶段转化率表（4）_.wmv144.34M
| | ├──06【了解】通过率表（1）_.wmv96.69M
| | ├──07【了解】通过率表（2）_.wmv113.06M
| | ├──08【理解】金融风控项目流程梳理_.wmv22.62M
| | ├──09【了解】下午内容介绍_.wmv5.09M
| | ├──10【理解】业务流程&ABC卡介绍_.wmv74.41M
| | ├──11【理解】互联网金融组成三部分_.wmv70.66M
| | ├──12【理解】机器学习流程_.wmv44.41M
| | ├──13【掌握】项目准备期-Y标签的定义_.wmv99.16M
| | ├──14【理解】项目准备期-样本的概述_.wmv53.22M
| | ├──15【理解】项目准备期-观察期和表现期_.wmv34.34M
| | ├──16【理解】姓名准备期-数据集划分_.wmv13.16M
| | ├──17【理解】项目准备期-样本设计_.wmv24.56M
| | ├──18【理解】特征工程-数据收集_.wmv58.50M
| | ├──19【理解】特征工程-特征构建_.wmv38.25M
| | ├──20【理解】特征工程-特征评估_.wmv20.09M
| | ├──21【理解】模型构建-模型训练&模型评估_.wmv21.25M
| | └──22【理解】上线运营_.wmv44.25M
| ├──day03
| | ├──01【回顾】昨日内容回顾_.wmv132.59M
| | ├──02【了解】今日内容介绍_.wmv17.38M
| | ├──03【理解】规则挖掘案例介绍_.wmv69.41M
| | ├──04【理解】业务规则案例-需求_.wmv18.94M
| | ├──05【理解】业务规则案例-数据字典_.wmv48.62M
| | ├──06【实现】业务规则案例-加载数据_.wmv74.00M
| | ├──07【实现】业务规则案例-数据处理（填充，过滤，去重）_.wmv115.59M
| | ├──08【实现】业务规则案例-数值型衍生_.wmv80.47M
| | ├──09【实现】业务规则案例-类别型衍生_.wmv34.03M
| | ├──10【实现】业务规则案例-模型训练&可视化_.wmv118.81M
| | ├──11【小结】业务规则案例-小结_.wmv52.19M
| | ├──12【理解】数据准备-征信数据介绍_.wmv110.09M
| | ├──13【了解】梳理数据内置逻辑_.wmv60.25M
| | ├──14【理解】时间截面特征&静态信息特征_.wmv57.06M
| | ├──15【理解】时间序列特征衍生方式_.wmv147.16M
| | ├──16【理解】时间序列特征缺失值处理_.wmv57.41M
| | ├──17【理解】时间序列特征未来信息处理_.wmv29.72M
| | ├──18【理解】分箱介绍_.wmv80.47M
| | ├──19【理解】卡方分箱_.wmv66.44M
| | ├──20【理解】toad库大致说明_.wmv34.66M
| | └──21【总结】今日内容回顾_.wmv59.53M
| ├──day04
| | ├──01【回顾】昨日内容回顾_.wmv96.34M
| | ├──02【实现】toad库分箱案例-加载数据_.wmv115.84M
| | ├──03【实现】toad库分箱案例-并可视化_.wmv143.66M
| | ├──04【实现】toad库分箱案例-调整箱数_.wmv58.00M
| | ├──05【实现】toad库分箱案例-其他分箱展示_.wmv15.22M
| | ├──06【实现】toad库分箱案例-WOE编码_.wmv68.66M
| | ├──07【实现】toad库分箱案例-badrate坏人的比例及调整_.wmv173.78M
| | ├──08【实现】toad库分箱案例-WOE编码_.wmv57.69M
| | ├──09【小结】toad库分箱案例-小结_.wmv57.84M
| | ├──10【理解】三种编码小结_.wmv26.53M
| | ├──11【了解】多值有序类型编码_.wmv11.22M
| | ├──12【了解】特征组合_.wmv17.75M
| | ├──13【了解】用户关联特征_.wmv27.50M
| | ├──14【扩展】信贷业务和可解释性_.wmv67.19M
| | ├──15【理解】好特征的标准-覆盖度_.wmv15.28M
| | ├──16【理解】好特征的标准-区分度_.wmv42.47M
| | ├──17【理解】好特征的标准-相关性_.wmv48.75M
| | ├──18【实现】好特征的标准-相关性案例_.wmv111.22M
| | ├──19【理解】好特征的标准-稳定性及小结_.wmv26.91M
| | ├──20【理解】特征筛选-星座特征_.wmv18.69M
| | ├──21【理解】特征筛选-Boruta_.wmv37.72M
| | ├──22【实现】特征筛选-Boruta案例_.wmv176.56M
| | └──23【总结】今日内容总结_.wmv93.28M
| ├──day05
| | ├──01【回顾】昨日内容回顾_.wmv39.12M
| | ├──02【理解】多特征筛选-方差膨胀系数_.wmv33.16M
| | ├──03【实现】多特征筛选-方差膨胀系数案例_.wmv187.94M
| | ├──04【实现】多特征筛选-递归特征消除_.wmv85.41M
| | ├──05【实现】多特征筛选-L1特征选择_.wmv76.19M
| | ├──06【理解】特征监控_.wmv65.97M
| | ├──07【小结】特征工程小结_.wmv52.56M
| | ├──08【了解】模型评分卡内容安排说明_.wmv18.59M
| | ├──09【了解】模型构建流程（上）_.wmv53.62M
| | ├──10【了解】模型构建流程（中）_.wmv47.56M
| | ├──11【理解】模型构建流程（下）_.wmv27.62M
| | ├──12【理解】逻辑回归评分卡-评分映射方法_.wmv58.56M
| | ├──13【实现】逻辑回归评分卡-加载数据_.wmv34.97M
| | ├──14【理解】逻辑回归评分卡-到底是先做特征还是先训练模型_.wmv42.81M
| | ├──15【实现】逻辑回归评分卡-模型训练_.wmv59.16M
| | ├──16【实现】逻辑回归评分卡-模型评估_.wmv108.34M
| | ├──17【实现】逻辑回归评分卡-特征筛选_.wmv226.16M
| | ├──18【理解】模型报告解读_.wmv106.81M
| | └──19【总结】今日内容总结_.wmv49.28M
| ├──day06
| | ├──01-昨日内容回顾_.wmv112.06M
| | ├──01【回顾】昨日内容回顾_.wmv116.78M
| | ├──02-评分卡逻辑转换_.wmv38.25M
| | ├──02【了解】今日内容介绍_.wmv39.84M
| | ├──03-评分卡转换_.wmv123.03M
| | ├──03【实现】逻辑回归评分卡-模型报告-数据准备_.wmv107.69M
| | ├──04-LightGBM优势_.wmv137.25M
| | ├──04【实现】逻辑回归评分卡-模型报告-报告实现（上）_.wmv129.41M
| | ├──05-LightGBM思路梳理_.wmv73.28M
| | ├──05【实现】逻辑回归评分卡-模型报告-报告实现（下）_.wmv134.09M
| | ├──06-集成学习评分卡_.wmv360.38M
| | ├──06【理解】逻辑回归评分卡-模型报告可视化_.wmv26.72M
| | ├──07-toad官网简介_.wmv142.44M
| | ├──07【实现】逻辑回归评分卡-评分映射_.wmv119.16M
| | ├──08-toad整体流程梳理_.wmv277.25M
| | ├──08【实现】逻辑回归评分卡-评级划分_.wmv45.34M
| | ├──09【理解】集成学习评分卡-LightGBM原理_.wmv119.44M
| | ├──10【回顾】上午内容回顾_.wmv41.28M
| | ├──11【理解】集成学习评分卡-LightGBM分布式含义解释_.wmv61.88M
| | ├──12【实现】集成学习评分卡-加载数据并数据划分_.wmv93.94M
| | ├──13【理解】LightGBM参数&训练思路分析_.wmv125.94M
| | ├──14【实现】集成学习评分卡-模型评估&画图_.wmv68.22M
| | ├──14【实现】集成学习评分卡-模型训练&特征筛选_.wmv228.94M
| | ├──15【实现】集成学习评分卡-评分映射_.wmv87.66M
| | ├──16【理解】集成学习评分卡-模型报告说明_.wmv93.16M
| | ├──17【理解】toad库梳理整个流程_.wmv415.53M
| | └──18【总结】今日内容总结_.wmv99.09M
| ├──day07
| | ├──01【回顾】昨日内容回顾_.wmv267.94M
| | ├──02【了解】今日内容介绍_.wmv11.75M
| | ├──03【理解】样本不均衡及处理方式说明_.wmv25.00M
| | ├──04【理解】样本不均衡-代价敏感介绍_.wmv31.12M
| | ├──05【实现】样本不均衡-代价敏感案例_.wmv90.91M
| | ├──06【理解】样本不均衡-过采样介绍_.wmv38.44M
| | ├──07【理解】样本不均衡-SMOTE案例流程说明_.wmv153.59M
| | ├──08【实现】杨不均衡-SMOTE案例实现_.wmv186.66M
| | ├──09【回顾】上午内容回顾_.wmv16.78M
| | ├──10【了解】反欺诈检测_.wmv50.38M
| | ├──11【了解】异常点检测说明_.wmv30.94M
| | ├──12【了解】异常点检测-zscore介绍_.wmv14.25M
| | ├──13【理解】异常点检测-LOF概述_.wmv57.56M
| | ├──14【实现】异常点检测-LOF案例_.wmv197.84M
| | ├──15【理解】异常点检测-IF概述_.wmv56.25M
| | ├──16【实现】异常点检测-IF案例_.wmv184.88M
| | ├──17【了解】preA模型_.wmv122.12M
| | └──18【小结】今日内容总结_.wmv203.81M
| └──day08
| | ├──01-git分支介绍_.wmv32.22M
| | ├──01【总结】项目总结-xmind_.wmv144.16M
| | ├──02-git配置_.wmv96.06M
| | ├──02【总结】项目总结-画图_.wmv100.88M
| | ├──03-PyCharm操作_.wmv81.78M
| | ├──03【了解】git简单历史_.wmv22.03M
| | ├──04-冲突解决_.wmv84.88M
| | ├──04【了解】版本控制系统简述_.wmv27.81M
| | ├──05-面试流程说明_.wmv186.16M
| | ├──05【了解】版本控制系统之集中式和分布式_.wmv32.88M
| | ├──06【了解】Git及安装_.wmv37.16M
| | ├──07【理解】概念区分_.wmv58.25M
| | ├──08【理解】git架构_.wmv55.28M
| | ├──09【了解】git分支_.wmv38.16M
| | ├──10【了解】gitee及分支描述_.wmv64.66M
| | ├──11【实现】配置账号及公钥_.wmv69.44M
| | ├──12【了解】git命令拉取代码_.wmv28.38M
| | ├──13【实现】PyCharm拉取代码_.wmv39.25M
| | ├──14【回顾】上午内容回顾_.wmv35.59M
| | ├──15【实操】PyCharm整合git操作_.wmv99.91M
| | ├──16【实操】更新远程仓库代码_.wmv36.16M
| | ├──17【实现】分支代码合并_.wmv57.09M
| | ├──18【理解】冲突的解决_.wmv78.38M
| | ├──19【了解】简历和项目文档概述_.wmv64.31M
| | └──20【理解】面试流程说明_.wmv125.72M
├──06_深度学习基础-V6.X版-6天-AI版
| ├──day01
| | ├──01-深度学习是什么_.wmv31.91M
| | ├──02-发展历史_.wmv39.91M
| | ├──03-torch简介_.wmv29.44M
| | ├──04-torch张量创建_.wmv57.53M
| | ├──05-线性张量和随机张量_.wmv24.69M
| | ├──06-全01张量_.wmv15.47M
| | ├──07-元素类型转换_.wmv11.47M
| | ├──08-张量和ndarray的转换_.wmv36.84M
| | ├──09-标量_.wmv9.78M
| | ├──10-基本运算_.wmv19.41M
| | ├──11-点乘和点积_.wmv24.16M
| | ├──12-运算函数_.wmv28.97M
| | ├──13-索引操作_.wmv27.75M
| | ├──14-范围与布尔索引_.wmv30.66M
| | ├──15-多维索引_.wmv19.53M
| | └──16-形状操作_.wmv20.47M
| ├──day02
| | ├──01-形状变换_.wmv49.56M
| | ├──02-view_.wmv24.94M
| | ├──03-张量拼接_.wmv25.72M
| | ├──04-自动微分_.wmv33.94M
| | ├──05-自动微分2_.wmv18.56M
| | ├──06-线性回归案例_.wmv89.12M
| | ├──07-回归案例总结_.wmv50.53M
| | ├──08-神经网络介绍_.wmv63.72M
| | ├──09-激活函数作用_.wmv25.81M
| | ├──10-sigmoid_.wmv36.25M
| | └──11-tanh+relu_.wmv43.03M
| ├──day03
| | ├──01-内容回顾_.wmv22.50M
| | ├──02-softmax_.wmv29.84M
| | ├──03-其他激活函数_.wmv21.56M
| | ├──04-激活函数总结_.wmv6.31M
| | ├──05-参数初始化_.wmv63.41M
| | ├──06-模型构建_.wmv71.88M
| | ├──07-参数量统计_.wmv34.81M
| | ├──08-神经网络优缺点_.wmv18.81M
| | ├──09-损失函数_.wmv57.06M
| | ├──10-交叉熵损失_.wmv25.59M
| | ├──11-二分类交叉熵损失_.wmv20.94M
| | ├──12-回归损失函数_.wmv40.19M
| | └──13-梯度下降算法_.wmv39.22M
| ├──day04
| | ├──01-内容回顾_.wmv42.19M
| | ├──02-前向和反向的过程_.wmv26.84M
| | ├──03-案例：前向过程_.wmv18.59M
| | ├──04-案例：反向输出层_.wmv25.59M
| | ├──05-案例：反向隐藏层_.wmv11.66M
| | ├──06-指数加权平均_.wmv69.19M
| | ├──07-动量法_.wmv28.56M
| | ├──08-adagrad_.wmv18.28M
| | ├──09-rmsprop+adam_.wmv11.09M
| | ├──10-学习率衰减_.wmv46.59M
| | ├──11-学习率衰减2_.wmv17.66M
| | ├──12-正则化方法_.wmv43.62M
| | ├──13-BN层_.wmv14.31M
| | ├──14-手机价格分类案例_.wmv79.28M
| | └──15-训练和预测_.wmv58.47M
| ├──day05
| | ├──01-图像是什么_.wmv52.09M
| | ├──02-卷积神经网络的构成_.wmv18.25M
| | ├──03-卷积层_.wmv83.22M
| | ├──04-卷积层的实现_.wmv28.91M
| | ├──05-池化层_.wmv41.56M
| | ├──06-图像分类案例_.wmv43.62M
| | ├──07-网络结构构建_.wmv32.28M
| | ├──08-网络构建实现_.wmv38.03M
| | ├──09-模型训练_.wmv39.47M
| | ├──10-自然语言处理概述_.wmv21.75M
| | ├──11-词嵌入层_.wmv77.31M
| | └──12-内容总结_.wmv7.53M
| └──day06
| | ├──01-RNN介绍_.wmv38.66M
| | ├──02-RNN的流程_.wmv19.78M
| | ├──03-API_.wmv47.97M
| | ├──04-歌词生成案例_.wmv107.03M
| | ├──05-数据集封装_.wmv39.91M
| | ├──06-模型构建_.wmv58.72M
| | ├──07-模型训练_.wmv51.25M
| | ├──08-模型预测_.wmv51.75M
| | └──09-内容总结_.wmv63.00M
├──07_自然语言处理+GPT-V6.X版-13天-AI版
| ├──day01
| | ├──01-课程安排_.wmv20.81M
| | ├──02-课程内容简介_.wmv16.28M
| | ├──03-自然语言处理入门_.wmv80.94M
| | ├──04-笔记总结_.wmv9.72M
| | ├──05-文本预处理的主要模块_.wmv19.19M
| | ├──06-文本分词的介绍_.wmv29.38M
| | ├──07-jieba精确模式分词_.wmv53.91M
| | ├──08-jieba全模式和搜索引擎分词_.wmv49.19M
| | ├──09-jieba繁体分词和用户自定义词典_.wmv49.81M
| | ├──10-上午内容回顾_.wmv37.94M
| | ├──11-NER和Pos的讲解_.wmv42.91M
| | ├──12-文本张量的表示方法介绍_.wmv54.91M
| | ├──13-onehot编码的实现和训练_.wmv100.44M
| | ├──14-onehot编码的应用_.wmv20.16M
| | ├──15-CBOW模型的思想_.wmv85.69M
| | ├──16-CBOW推导过程思想_.wmv49.50M
| | └──17-今日内容总结_.wmv19.31M
| ├──day02
| | ├──01-昨日内容回顾_.wmv56.72M
| | ├──02-skipgram的讲解_.wmv40.66M
| | ├──03-fasttext训练词向量基础_.wmv98.75M
| | ├──03-fasttext训练词向量进阶_.wmv63.78M
| | ├──04-nn.Embedding和word2vec区别_.wmv12.50M
| | ├──05-nn.Embedding代码分析_.wmv46.78M
| | ├──06-nn.Embedding的代码实现_.wmv167.47M
| | ├──07-从Embedding中获取某个词的词向量_.wmv60.53M
| | ├──08-标签数量统计分布_.wmv65.19M
| | ├──09-句子长度分布统计_.wmv105.47M
| | ├──10-长度分布散点图_.wmv40.59M
| | ├──11-词频统计代码实现_.wmv45.81M
| | └──12-今日内容总结_.wmv23.06M
| ├──day03
| | ├──01-昨日内容回顾_.wmv57.22M
| | ├──02-词云展示讲解前半部分_.wmv116.50M
| | ├──03-词云展示讲解后半部分_.wmv48.69M
| | ├──04-添加N-gram特征的原理_.wmv50.97M
| | ├──05-实现N-Gram的代码_.wmv61.06M
| | ├──06-句子长短补齐和截断_.wmv49.75M
| | ├──07-回忆数据增强方法_.wmv29.97M
| | ├──08-RNN模型入门_.wmv86.50M
| | ├──09-传统RNN模型内部结构讲解_.wmv54.53M
| | ├──10-RNN模型代码的实现--base_.wmv95.34M
| | ├──11-RNN模型改变长度_.wmv30.53M
| | ├──12-RNN模型原理解析_.wmv41.75M
| | ├──13-RNN模型修改层数_.wmv50.53M
| | └──14-今日内容总结_.wmv23.75M
| ├──day04
| | ├──01-昨日内容回顾_.wmv72.94M
| | ├──02-LSTM模型内部结构分析_.wmv71.62M
| | ├──03-LSTM模型内部结构源代码分析_.wmv26.16M
| | ├──04-Bi-LSTM模型原理_.wmv44.22M
| | ├──05-LSTM模型代码的实现_.wmv94.84M
| | ├──06-GRU模型架构原理解析_.wmv52.47M
| | ├──07-GRU模型代码的实现_.wmv33.91M
| | ├──08-RNN人名分类案例介绍_.wmv64.06M
| | ├──09-RNN人名分类导入第三方工具_.wmv42.66M
| | ├──10-将文本数据读取到内存中_.wmv62.72M
| | ├──11-构建Dataset数据源对象_.wmv108.91M
| | ├──12-实例化dataloader对象_.wmv61.97M
| | └──13-今日内容总结_.wmv22.56M
| ├──day05
| | ├──01-昨日内容总结_.wmv55.03M
| | ├──02-RNN模型的搭建和测试_.wmv146.78M
| | ├──03-LSTM模型的搭建和测试_.wmv50.62M
| | ├──04-GRU模型的搭建和测试_.wmv21.66M
| | ├──05-RNN模型训练代码的实现--前半部分_.wmv119.00M
| | ├──06-RNN模型训练代码的实现--后半部分_.wmv104.62M
| | ├──07-LSTM模型的训练代码的实现_.wmv51.84M
| | ├──08-保存模型训练结果到文件_.wmv86.03M
| | ├──09-模型结果的图形化展示_.wmv104.31M
| | ├──10-RNN模型预测结果_.wmv121.12M
| | ├──11-LSTM+GRU模型预测_.wmv36.56M
| | └──12-今日内容总结_.wmv50.06M
| ├──day06
| | ├──01-昨日内容回顾_.wmv52.03M
| | ├──02-seq2seq文本翻译过程解析_.wmv77.12M
| | ├──03-深度学习注意力机制介绍_.wmv54.03M
| | ├──04-不带Attention的Encoder2Decoder框架解析_.wmv44.38M
| | ├──05-带Attention的Encoder2Decoder框架解析_.wmv64.06M
| | ├──06-注意力概率分布的计算方式_.wmv34.56M
| | ├──07-softAttention的讲解_.wmv47.69M
| | ├──08-hardAttention和softAttention的介绍_.wmv32.53M
| | ├──09-seq2seq框架加入attention计算过程解释_.wmv61.19M
| | ├──10-pytorch版本的attention计算过程_.wmv68.09M
| | ├──11-注意力计算规则_.wmv37.84M
| | ├──12-三维矩阵乘法解析_.wmv56.41M
| | ├──13-注意力的作用和计算步骤_.wmv44.31M
| | ├──14-注意力机制实现代码的讲解_.wmv73.16M
| | ├──15-注意力机制代码的实现_.wmv83.72M
| | ├──16-注意力机制实现扩展_.wmv44.88M
| | ├──17-今日内容总结_.wmv38.06M
| | └──18-seq2seq英译法案例分析_.wmv15.69M
| ├──day07
| | ├──01-昨日内容回顾_.wmv54.66M
| | ├──02-英译法案例基本介绍_.wmv117.50M
| | ├──03-数据清洗函数定义_.wmv60.47M
| | ├──04-get_data函数获取my_pairs对_.wmv70.91M
| | ├──05-get_data函数获取英文和法文词典_.wmv73.75M
| | ├──06-Dataset类的实现_.wmv68.78M
| | ├──07-Dataloader类的实现_.wmv39.44M
| | ├──08-基于GRU的编码器代码实现_.wmv66.94M
| | ├──09-基于GRU的无Attention的解码器代码分析_.wmv87.00M
| | ├──10-基于GRU的无Attention的代码实现和测试_.wmv101.50M
| | ├──11-基于GRU的带Attention的代码分析_.wmv136.06M
| | ├──12-基于GRU解码器代码的再次分析_.wmv53.03M
| | └──13-模型训练代码的分析_.wmv169.31M
| ├──day08
| | ├──01-昨日内容回顾_.wmv42.00M
| | ├──02-模型训练函数前半部分_.wmv102.66M
| | ├──03-模型内部训练函数前半部分_.wmv74.25M
| | ├──04-模型内部训练函数后半部分_.wmv83.59M
| | ├──05-模型训练函数后半部分_.wmv75.50M
| | ├──06-模型评估函数代码分析_.wmv74.88M
| | ├──07-模型测试函数实现_.wmv80.28M
| | ├──08-模型测试函数代码实现_.wmv143.69M
| | ├──09-模型评估函数_.wmv43.22M
| | ├──10-注意力绘图_.wmv68.56M
| | ├──11-transformer背景介绍_.wmv21.16M
| | ├──12-transformer模型架构_.wmv68.75M
| | ├──13-输入部分Embedding代码的实现_.wmv49.34M
| | ├──14-三角函数位置编码解析_.wmv87.59M
| | └──15-位置编码代码分析_.wmv109.56M
| ├──day09
| | ├──01-昨日内容回顾_.wmv33.12M
| | ├──02-位置编码代码分析_.wmv87.94M
| | ├──03-位置编码器的代码实现_.wmv53.25M
| | ├──04-三角函数位置编码的图形化展示_.wmv58.19M
| | ├──05-下三角矩阵的代码实现_.wmv65.31M
| | ├──06-注意力机制代码的分析_.wmv103.38M
| | ├──07-mask机制的讲解_.wmv47.62M
| | ├──08-注意力机制代码的实现_.wmv88.53M
| | ├──09-多头注意力的思想_.wmv78.69M
| | ├──10-多头注意力机制代码实现的思路分析_.wmv120.44M
| | ├──11-多头注意力机制代码的实现_.wmv117.31M
| | ├──12-前馈全连接层代码的实现_.wmv54.50M
| | ├──13-规范化层代码的分析_.wmv40.72M
| | ├──14-规范化层代码的实现_.wmv30.91M
| | ├──15-LayerNorm和BatchNorm的区别_.wmv16.25M
| | └──16-今日内容总结_.wmv32.84M
| ├──day10
| | ├──01-编码器子层连接结构实现_.wmv109.69M
| | ├──02-编码器层代码的实现_.wmv63.03M
| | ├──03-编码器代码的实现_.wmv35.66M
| | ├──04-解码器层代码的实现_.wmv80.47M
| | ├──05-解码器层代码的测试_.wmv53.41M
| | ├──06-解码器的代码实现_.wmv33.25M
| | ├──07-输出部分代码的实现_.wmv25.31M
| | ├──08-transformer模型架构代码分析_.wmv41.69M
| | ├──09-EncoderDecoder架构代码实现_.wmv95.28M
| | ├──10-EncoderDecoder模型实例化代码分析_.wmv42.59M
| | ├──11-transformer模型架构的实现和测试_.wmv87.66M
| | ├──12-fasttext工具的介绍_.wmv23.38M
| | ├──13-层次softmax的哈夫曼树的构建_.wmv64.47M
| | ├──14-层次softmax进行模型训练的原理_.wmv47.47M
| | ├──15-负采样优化算法原理_.wmv33.50M
| | └──16-今日内容总结_.wmv26.06M
| ├──day11
| | ├──01-文本分类任务的介绍_.wmv78.91M
| | ├──02-fasttext文本分类数据获取和分割_.wmv61.09M
| | ├──03-fasttext实现文本分类未调优_.wmv36.53M
| | ├──04-数据优化后进行文本分类_.wmv49.19M
| | ├──05-调整学习率-epoch等参数优化模型_.wmv64.28M
| | ├──06-模型超参数调优_.wmv23.34M
| | ├──07-词向量迁移介绍_.wmv26.28M
| | ├──08-迁移学习的概念_.wmv31.53M
| | ├──09-预训练模型的介绍_.wmv40.78M
| | ├──10-transformers库的基本介绍_.wmv40.16M
| | ├──11-transformers库的使用_.wmv33.47M
| | ├──12-transformers库使用的基本方式_.wmv49.72M
| | ├──13-pipeline方式实现文本分类_.wmv89.16M
| | ├──14-pipeline方式实现特征抽取_.wmv67.84M
| | ├──15-pipeline方式实现完形填空_.wmv39.41M
| | ├──16-pipeline方式实现阅读理解任务_.wmv29.44M
| | ├──17-pipeline方式实现文本摘要任务_.wmv42.88M
| | ├──18-pipeline方式实现NER任务_.wmv61.88M
| | ├──19-automodel实现文本分类_.wmv128.31M
| | └──20-今日内容总结_.wmv31.31M
| ├──day12
| | ├──01-昨日内容回顾_.wmv39.50M
| | ├──02-AutoModel实现特征提取任务_.wmv105.84M
| | ├──03-AutoModel实现完形填空任务_.wmv67.22M
| | ├──04-AutoModel实现阅读理解任务_.wmv83.34M
| | ├──05-AutoModel实现文本摘要任务_.wmv136.94M
| | ├──06-AutoModel实现NER任务_.wmv99.25M
| | ├──07-具体模型实现完形填空任务_.wmv44.59M
| | ├──08-迁移学习案例基本介绍_.wmv61.56M
| | ├──09-中文分类案例数据加载_.wmv94.16M
| | ├──10-中文分类案例自定义函数实现_.wmv148.03M
| | ├──11-中文分类案例模型搭建_.wmv75.94M
| | ├──12-中文分类案例模型训练思路_.wmv24.47M
| | ├──13-中文分类案例模型训练代码_.wmv103.38M
| | ├──14-中文分类案例模型评估代码_.wmv82.31M
| | └──15-今日内容总结_.wmv59.03M
| └──day13
| | ├──01-中文完型填空数据预处理_.wmv121.09M
| | ├──02-中文完型填空构建模型_.wmv56.41M
| | ├──03-中文完型填空模型训练_.wmv41.12M
| | ├──04-中文完型填空模型预测_.wmv35.16M
| | ├──05-中文句子关系构建dataset对象_.wmv75.72M
| | ├──06-中文句子关系构建自定义函数_.wmv58.84M
| | ├──07-中文句子关系模型训练_.wmv40.44M
| | ├──08-中文句子关系模型完结_.wmv40.09M
| | ├──09-BERT模型的架构_.wmv86.69M
| | ├──10-BERT模型的预训练任务_.wmv43.03M
| | ├──11-BERT模型的优缺点_.wmv33.44M
| | ├──12-BERT模型的特点_.wmv18.16M
| | ├──13-AlBERT模型的介绍_.wmv52.50M
| | ├──14-Roberta模型的介绍_.wmv25.53M
| | ├──15-MacBERT和SpanBERT的介绍_.wmv35.69M
| | ├──16-ELMO模型的介绍_.wmv55.59M
| | ├──17-GPT模型的介绍_.wmv50.62M
| | ├──18-BERT_GPT_ELMO的对比_.wmv13.09M
| | └──19-今日内容总结_.wmv117.94M
├──08_知识图谱-V6.X-10天-AI版
| ├──day01
| | ├──01-什么是知识图谱_.wmv353.84M
| | ├──02-知识图谱技术概况_.wmv647.38M
| | ├──03-三个工具_.wmv215.78M
| | ├──04-doccano安装_.wmv216.03M
| | ├──05-doccano使用1_.wmv253.28M
| | ├──06-doccano使用2_.wmv69.06M
| | ├──07-总结_.wmv134.69M
| | ├──08-基于规则_.wmv394.50M
| | ├──09-ner基本知识_.wmv350.62M
| | ├──10-基于规则案例_.wmv462.38M
| | └──11-lstm+crf架构_.wmv1.01G
| ├──day02
| | ├──01-CRF损失函数推导_.wmv923.66M
| | ├──02-代码架构_.wmv439.75M
| | ├──03-项目架构_.wmv203.44M
| | ├──04-加载数据集_.wmv227.56M
| | ├──05-transfer方法_.wmv526.66M
| | ├──06-read_label_text_.wmv264.09M
| | ├──07-总结_.wmv189.31M
| | ├──08-config_.wmv258.16M
| | ├──09-dataset-collate_fn_.wmv525.19M
| | ├──10-get_data_.wmv139.59M
| | └──11-总结_.wmv178.34M
| ├──day03
| | ├──01-每日反馈+总结_.wmv419.97M
| | ├──02-lstm搭建_.wmv366.03M
| | ├──03-lstm-crf搭建_.wmv589.25M
| | ├──04-model2train_.wmv621.03M
| | ├──06-model2dev_.wmv654.72M
| | ├──07-model2text_.wmv425.41M
| | ├──08-extract_ents_.wmv423.53M
| | ├──09-部署上线_.wmv148.94M
| | ├──10-TransferData-debug_.wmv436.56M
| | ├──11-dataloader-dubug_.wmv321.91M
| | └──12-train-debug_.wmv254.34M
| ├──day04
| | ├──01-每日反馈+拓展_.wmv464.66M
| | ├──02-关系抽取基本知识_.wmv343.97M
| | ├──03-基于规则实现RE_.wmv390.56M
| | ├──04-config_.wmv849.41M
| | ├──05-数据预处理1_.wmv415.94M
| | ├──06-sent_padding_.wmv76.75M
| | ├──07-pos_padding_.wmv90.62M
| | ├──08-get_data_.wmv365.91M
| | ├──09-dataset_.wmv111.31M
| | ├──10-collate_fn_.wmv211.03M
| | └──11-get_loader_.wmv339.09M
| ├──day05
| | ├──01-每日反馈+总结_.wmv431.47M
| | ├──02-模型init_.wmv383.19M
| | ├──03-forward的shape变化_.wmv214.69M
| | ├──04-forward实现_.wmv218.72M
| | ├──05-train实现_.wmv442.50M
| | ├──06-model2test实现_.wmv177.53M
| | ├──07-predict讲解_.wmv200.88M
| | ├──08-casrel架构_.wmv367.19M
| | ├──09-casrel模型细节_.wmv257.47M
| | └──10-config_.wmv619.97M
| ├──day06
| | ├──01-每日反馈+总结_.wmv257.78M
| | ├──02-find_head_index_.wmv90.25M
| | ├──03-label初始化_.wmv234.56M
| | ├──04-label举例解释_.wmv88.62M
| | ├──05-解析inner_triples_.wmv400.34M
| | ├──06-填充工作_.wmv558.66M
| | ├──07-collate_fn_.wmv519.12M
| | ├──08-dataset_.wmv168.84M
| | ├──09-get_data+debug_.wmv692.75M
| | └──10-模型init_.wmv192.53M
| ├──day07
| | ├──01-反馈+总结_.wmv94.22M
| | ├──02-get_subs+get_objs_for_specific_sub_.wmv334.12M
| | ├──03-compute_loss_.wmv163.44M
| | ├──04-loss_.wmv220.75M
| | ├──05-load_model_.wmv419.81M
| | ├──06-extract_sub_.wmv174.91M
| | ├──07-extract_obj_and_rel_.wmv196.56M
| | ├──08-train_.wmv363.09M
| | ├──09-train_debug_.wmv1.26G
| | └──10-predict_.wmv568.91M
| ├──day08
| | ├──01-neo4切换测试库_.wmv378.59M
| | ├──02-cypher使用2_.wmv95.28M
| | ├──03-cypher使用3_.wmv262.88M
| | ├──04-创建节点关系_.wmv100.34M
| | ├──05-查询节点关系_.wmv403.78M
| | ├──06-get_spo_type分析_.wmv436.88M
| | ├──07-ready_data_.wmv214.16M
| | ├──08-构建neo4j_.wmv567.19M
| | └──09-检索neo4j_.wmv307.88M
| └──day09
| | └──01-模型debug_.wmv1010.28M
├──09_大模型-V6.X版本【线下】-13天-AI版
| ├──day01
| | ├──01-大模型背景_.wmv81.75M
| | ├──02-语言模型_.wmv48.38M
| | ├──03-语言模型的发展_.wmv7.94M
| | ├──04-n-gram_.wmv99.09M
| | ├──06-神经网络的语言模型_.wmv84.59M
| | ├──07-bleu_.wmv143.88M
| | ├──08-rough_.wmv41.22M
| | ├──09-PPL_.wmv112.72M
| | └──10-AE的BERT模型_.wmv233.81M
| ├──day02
| | ├──01-GPT的网络结构_.wmv123.94M
| | ├──02-GPT的预训练过程_.wmv92.19M
| | ├──03-GPT的微调_.wmv108.91M
| | ├──04-AR的特点_.wmv39.91M
| | ├──05-seq2seq_.wmv152.41M
| | ├──06-GPT2网络和训练_.wmv263.28M
| | ├──07-GPT2的特点_.wmv49.50M
| | ├──08-GPT3的网络结构_.wmv128.81M
| | ├──09-in_contextlearning_.wmv116.31M
| | ├──10-GPT3的特点_.wmv75.66M
| | └──11-强化学习_.wmv197.88M
| ├──day03
| | ├──01-chatGPT的微调方法_.wmv121.91M
| | ├──02- chatGpt的微调方法_.wmv155.88M
| | ├──03-GLM的训练目标_.wmv148.47M
| | ├──04-GLM的位置编码_.wmv40.72M
| | ├──05-GLM的特点_.wmv253.66M
| | ├──06-LLaMa和Bloom_.wmv228.09M
| | ├──07-百川大模型_.wmv139.19M
| | ├──08-提示词工程_.wmv25.28M
| | └──09-提示词工程原则_.wmv426.38M
| ├──day04
| | ├──01-项目背景_.wmv192.09M
| | ├──02-文本分类提示词_.wmv174.38M
| | ├──03-文本分类推理_.wmv140.97M
| | ├──04-趋动云使用_.wmv273.75M
| | ├──05-文本信息抽取提示词_.wmv184.84M
| | ├──06-文本信息抽取的后处理_.wmv34.25M
| | ├──07-文本抽取的实现_.wmv315.34M
| | └──08-文本匹配的内容_.wmv163.50M
| ├──day05
| | ├──01-NLP的四范式_.wmv295.94M
| | ├──02-Prompt微调的方式_.wmv152.25M
| | ├──03-PET微调方法_.wmv130.38M
| | ├──04-硬模版和软模版_.wmv65.38M
| | ├──05-prompt tuning_.wmv154.91M
| | ├──06-p-tuning_.wmv84.00M
| | ├──07-PPL_.wmv81.47M
| | └──08-prompt-tuning总结_.wmv66.22M
| ├──day06
| | ├──01-prefix微调_.wmv211.59M
| | ├──02-adapter_.wmv86.31M
| | ├──03-lora_.wmv183.78M
| | ├──04-伪代码_.wmv38.16M
| | ├──05-项目背景和数据_.wmv371.84M
| | ├──06-process实现_.wmv191.03M
| | ├──07-dataset实现_.wmv79.91M
| | ├──08-dataloader_.wmv143.19M
| | ├──09-模型构建_.wmv99.00M
| | └──10-config_.wmv30.62M
| ├──day07
| | ├──01-模型训练_.wmv373.50M
| | ├──02-模型训练2_.wmv340.44M
| | ├──03-模型训练2_.wmv160.34M
| | ├──04-模型验证_.wmv84.28M
| | ├──05-准确率计算_.wmv146.41M
| | ├──06-损失函数_.wmv83.09M
| | ├──07-topK 和topP_.wmv95.62M
| | ├──08-模型预测_.wmv383.97M
| | ├──09-topK和topP_.wmv102.41M
| | └──10-前端部署_.wmv21.78M
| ├──day08
| | ├──01-项目背景_.wmv40.19M
| | ├──02-PET的项目架构_.wmv91.25M
| | ├──03-template构建1_.wmv217.25M
| | ├──04-template构建2_.wmv194.78M
| | ├──05-dataset_.wmv30.25M
| | ├──06-data_preprocess_.wmv211.88M
| | ├──07-dataloader_.wmv39.03M
| | ├──08-标签词映射_.wmv44.72M
| | ├──09-主标签找子标签_.wmv118.53M
| | └──10-子标签找主标签_.wmv75.94M
| ├──day09
| | ├──01-损失计算_.wmv239.56M
| | ├──02-id转换_.wmv83.62M
| | ├──03-评价指标_.wmv65.66M
| | ├──04-模型训练_.wmv91.72M
| | ├──05-模型预测_.wmv35.19M
| | ├──06-ptuning的数据处理_.wmv388.56M
| | ├──07-ptuning的dataloader_.wmv24.78M
| | └──08-ptuning的工具函数、训练和预测_.wmv119.31M
| ├──day10
| | ├──01-项目介绍_.wmv135.28M
| | ├──02-配置信息_.wmv299.22M
| | ├──03-数据预处理_.wmv29.53M
| | ├──04-data_loader_.wmv99.53M
| | ├──05-工具函数_.wmv43.69M
| | ├──06-模型训练_.wmv154.34M
| | └──07-模型预测_.wmv53.28M
| ├──day11
| | ├──01-Langchain介绍_.wmv180.00M
| | ├──02-Chat模型_.wmv57.66M
| | ├──03-嵌入模型_.wmv48.00M
| | ├──04-prompts_.wmv102.28M
| | ├──05-chains_.wmv79.66M
| | ├──06-agents_.wmv92.69M
| | ├──07-memory_.wmv83.44M
| | ├──08-index_.wmv50.75M
| | ├──09-向量数据库_.wmv49.94M
| | └──10-检索器_.wmv94.31M
| ├──day12
| | ├──01-RAG_.wmv152.31M
| | ├──02-模型构建_.wmv44.03M
| | ├──03-向量库构建和检索_.wmv51.50M
| | ├──04-Function——Call_.wmv66.16M
| | ├──05-原理_.wmv116.66M
| | ├──06-实践_.wmv95.34M
| | ├──07-SQL_.wmv82.75M
| | ├──08-GPTs_.wmv158.06M
| | ├──09-AssistantAPI的使用_.wmv86.62M
| | └──10-AssistantAPI实践_.wmv126.59M
| └──day13
| | ├──01-agent_.wmv188.53M
| | ├──02-Agent开发工具_.wmv216.56M
| | └──03-内容总结_.wmv105.19M
├──10_开源大模型平台-V6.X版本-3天-AI版
| ├──01-讯飞
| | ├──01-星火大模型介绍_.wmv68.09M
| | ├──02-API_.wmv65.12M
| | ├──03-大模型定制平台_.wmv59.00M
| | └──04-语音大模型_.wmv58.28M
| ├──02-百度
| | ├──01-百度千帆_.wmv74.69M
| | └──02-千帆模型_.wmv58.12M
| └──03-阿里
| | ├──01-阿里百炼_.wmv90.28M
| | └──02-阿里PAI_.wmv99.31M
├──阶段011 赠品-投满分项目
| ├──day01
| | ├──01-项目背景和数据集介绍_.wmv91.56M
| | ├──02-数据集获取_.wmv41.06M
| | ├──03-数据分布分析_.wmv25.56M
| | ├──04-分词_.wmv35.78M
| | ├──05-数据获取_.wmv29.00M
| | ├──06-特征工程_.wmv29.19M
| | ├──07-模型构建与训练_.wmv19.19M
| | ├──08-fasttext数据处理_.wmv39.62M
| | ├──09-fasttext数据集构建_.wmv44.66M
| | ├──10-fasttext模型训练_.wmv19.47M
| | └──11-优化1-自动化参数搜索_.wmv30.66M
| ├──day02
| | ├──01-fasttext优化-分词_.wmv20.88M
| | ├──02-模型训练_.wmv13.97M
| | ├──03-模型部署_.wmv73.25M
| | ├──04-bert数据信息_.wmv54.44M
| | ├──05-bert代码结构构建_.wmv12.75M
| | ├──06-bert数据获取_.wmv174.50M
| | ├──07-数据迭代_.wmv124.84M
| | └──08-时间差计算_.wmv12.53M
| ├──day03
| | ├──01-模型构建_.wmv256.00kb
| | ├──02-模型训练与评估思想_.wmv43.59M
| | ├──03-模型训练与评估实现_.wmv98.62M
| | ├──04-实现2_.wmv92.12M
| | ├──05-模型预测_.wmv88.16M
| | ├──06-模型部署_.wmv35.41M
| | └──07-模型量化_.wmv40.50M
| ├──day04
| | ├──01-昨日回顾_.wmv48.59M
| | ├──02-模型蒸馏思想_.wmv64.62M
| | ├──03-模型蒸馏项目架构_.wmv16.53M
| | ├──04-词表构建_.wmv79.81M
| | ├──05-数据获取_.wmv109.44M
| | ├──06-数据获取实现_.wmv17.50M
| | ├──07-数据迭代实现_.wmv67.22M
| | └──08-textCNN实践_.wmv102.62M
| ├──day05
| | ├──01-内容回顾_.wmv20.03M
| | ├──02-textCNN介绍_.wmv67.53M
| | ├──03-数据对齐_.wmv36.69M
| | ├──04-损失计算_.wmv57.72M
| | ├──05-模型训练_.wmv75.41M
| | ├──06-训练流程_.wmv41.34M
| | ├──06-主函数_.wmv37.75M
| | ├──07-剪枝思想_.wmv14.84M
| | ├──08-特定层剪枝_.wmv65.16M
| | ├──09-结构化剪枝_.wmv14.53M
| | ├──10-多层剪枝_.wmv41.06M
| | ├──11-全局剪枝_.wmv35.06M
| | └──12-自定义剪枝_.wmv27.59M
| └──day06
| | ├──01-面试问题和工作文问题_.wmv78.47M
| | ├──02-数据集构建方法_.wmv9.38M
| | └──03-项目串讲_.wmv21.38M
├──阶段012 赠品-计算机视觉
| └──此部分为赠送教程-CV
| | ├──Opencv视频教程
| | └──《OpenCV3编程入门》书本配套源代码
├──阶段013 赠品-亿图人脸支付项目
| ├──01-人脸检测
| | ├──01.内容回顾_.wmv32.66M
| | ├──02.视频读写_.wmv77.84M
| | ├──03.人脸检测概述_.wmv56.94M
| | ├──04.验证数据集_.wmv104.56M
| | ├──05.数据集获取_.wmv87.50M
| | ├──06.模型构建_.wmv175.81M
| | ├──07.参数配置_.wmv66.22M
| | ├──08.训练策略_.wmv38.28M
| | ├──09.训练流程_.wmv103.09M
| | ├──10.模型训练_.wmv43.41M
| | └──11.内容总结_.wmv15.53M
| ├──02-人脸姿态
| | ├──01.内容回顾_.wmv41.91M
| | ├──02.模型训练结果_.wmv47.84M
| | ├──03.模型预测_.wmv46.28M
| | ├──04.模型预测流程_.wmv144.28M
| | ├──05.人脸姿态概述_.wmv69.53M
| | ├──06.数据集加载_.wmv58.84M
| | ├──07.数据增强_.wmv178.53M
| | ├──08.模型构建_.wmv116.47M
| | ├──09.模型训练_.wmv115.06M
| | └──10. 内容总结_.wmv13.62M
| ├──03-人脸多任务(1)
| | ├──01.内容回顾_.wmv20.75M
| | ├──02.人脸多任务_.wmv139.25M
| | ├──03.数据加载_.wmv186.16M
| | ├──04.数据增强_.wmv37.19M
| | ├──05.模型构建_.wmv20.78M
| | ├──06.模型训练_.wmv99.28M
| | ├──07.模型预测_.wmv54.72M
| | ├──08.人脸识别_.wmv85.50M
| | ├──09.数据获取_.wmv48.31M
| | ├──10.模型构建_.wmv110.56M
| | ├──11.arcface_.wmv76.84M
| | └──12.内容总结_.wmv14.38M
| └──04-人脸识别
| | ├──01.内容回顾_.wmv18.28M
| | ├──02.模型训练_.wmv88.00M
| | ├──03.模型使用_.wmv197.66M
| | ├──04.模型集成_.wmv77.72M
| | ├──05.代码结构_.wmv39.09M
| | ├──06.人脸矫正_.wmv82.62M
| | ├──07.属性获取_.wmv47.28M
| | ├──08.可视化_.wmv207.53M
| | ├──09.模型部署_.wmv56.59M
| | └──10.人脸支付项目总结_.wmv49.78M
└──阶段014 赠品-AI智慧交通项目实战
| ├──01-opencv
| | ├──01-项目架构_.wmv20.38M
| | ├──02-项目构成_.wmv15.47M
| | ├──03-资料共享_.wmv7.97M
| | ├──04-opencv介绍_.wmv20.81M
| | ├──05-图像读写_.wmv36.22M
| | ├──06-绘制几何图像_.wmv47.34M
| | ├──07-图像加法_.wmv39.31M
| | ├──08-图像缩放与平移_.wmv61.66M
| | ├──09-图像旋转和仿射变换_.wmv46.16M
| | ├──10-透射变换_.wmv19.69M
| | ├──11-图像噪声_.wmv15.09M
| | ├──12-图像平滑方法_.wmv87.34M
| | ├──13-边缘检测思想_.wmv94.12M
| | ├──14-sobel边缘检测_.wmv22.28M
| | ├──15-canny边缘检测_.wmv46.06M
| | ├──16-视频读写_.wmv53.16M
| | └──17-opencv总结_.wmv65.88M
| ├──02-yoloV8
| | ├──01-YOLO发展_.wmv15.22M
| | ├──02-V8简介_.wmv23.03M
| | ├──03-V8的使用_.wmv68.06M
| | ├──04-效果展示_.wmv25.50M
| | └──05-streamlit的实现_.wmv78.88M
| ├──03-车流量统计
| | ├──01-车流量统计思想_.wmv33.19M
| | ├──02-多目标跟踪算法_.wmv77.56M
| | ├──03-sort和deepsort算法_.wmv36.53M
| | ├──04-KM算法_.wmv43.97M
| | ├──05-卡尔曼滤波_.wmv83.47M
| | ├──06-卡尔曼滤波思想_.wmv108.28M
| | ├──07-卡尔曼滤波实践_.wmv108.41M
| | ├──08-sort算法实现1 _.wmv106.28M
| | ├──09-sort算法实现2_.wmv61.19M
| | ├──10-sort算法实现跟踪_.wmv115.78M
| | └──11-deepsort算法跟踪_.wmv26.62M
| └──04-车道线检测
| | ├──01-车道线检测原理_.wmv65.59M
| | ├──02-相机坐标系转换_.wmv80.09M
| | ├──03-内容回顾_.wmv117.00M
| | ├──04-相机较正方法_.wmv119.00M
| | ├──05-优化方法_.wmv105.97M
| | ├──06-优化方法2_.wmv112.94M
| | ├──07-相机较正流程_.wmv28.06M
| | ├──08-双目较正_.wmv21.53M
| | ├──09-相机较正实现_.wmv132.81M
| | ├──10-图像去畸变_.wmv28.16M
| | ├──11-车道线提取_.wmv55.69M
| | ├──12-车道线定位_.wmv74.62M
| | ├──13-车道线拟合_.wmv96.66M
| | ├──14-车道线填充_.wmv25.66M
| | ├──15-车道线曲率_.wmv62.56M
| | ├──16-车辆偏离中心库里计算_.wmv38.59M
| | ├──17-车道线检测流程_.wmv31.69M
| | └──18-效果展示_.wmv14.50M
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