zoukankan      html  css  js  c++  java
  • 李宏毅机器学习

    ML Lecture 0: Introduction of Machine Learning

    1. AI v.s. ML v.s. DL
      • Artificial intelligence -> objective
      • Machine learning -> methods
      • Deep learning -> one method of machine learning
    2. Hand-crafted rules
      • many "ifs"
      • hard to consider all possibilities
      • no learning (limited)
      • lots of human efforts (not suitable for small industry)
    3. Machine learning
      • write programs for learning
      • looking for a function from data
        • 1. defining a set of functions -> model
        • 2. training data -> evaluate the goodness of functions (supervised learning)
        • 3. picking the best function f star
        • 4. using f star
      • 1-3 -> traing 4 -> testing
    4. Learning map
    5. Regression (output: scalar/value/real number)
      • e.g. prediction of PM2.5
    6. Classification  
      • binary classification (e.g. spam filtering)
      • multi-class classification (e.g. document classification)
    7. Supervised learning
      • training data: input / output pair of target function (function output -> label)
      • hard to collect a large amount of labelled data -> semi-supervised learning / tramsfer learning / unsuperviesd learning / reinforcement learning
    8. Structured learning - beyond classification
      • e.g. speech recognition / machine translation
    9. Reinforcement learning
      • no correct answers but only critics (评价)
  • 相关阅读:
    GitLab基本用法
    SSH免密登录详解
    一文搞懂GitLab安装部署及服务配置
    初识:LevelDB
    Jenkins安装与Gitlab项目部署详解
    CentOS7的安装和配置
    C/C++语言的学习方向
    C语言atoi函数
    C语言整数的取值范围
    C语言scanf函数
  • 原文地址:https://www.cnblogs.com/aintro/p/8570799.html
Copyright © 2011-2022 走看看