Gradient Descent For Linear Regression

Loading...
斯坦福大学
4.9(119,573 個評分) | 2.6M 名學生已註冊
查看授課大綱

您將學習的技能

Logistic Regression, Artificial Neural Network, Machine Learning (ML) Algorithms, Machine Learning

審閱

4.9(119,573 個評分)
  • 5 stars
    110,725 ratings
  • 4 stars
    8,155 ratings
  • 3 stars
    515 ratings
  • 2 stars
    87 ratings
  • 1 star
    91 ratings
SB

Sep 27, 2018

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

MN

Oct 31, 2017

Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.

從本節課中
Linear Regression with One Variable
Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.

教學方

  • Andrew Ng

    Andrew Ng

    CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain

探索我們的目錄

免費加入並獲得個性化推薦、更新和優惠。