Gradient Descent in Practice II - Learning Rate

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Stanford University
4.9 (111,312 ratings) | 2.5M Students Enrolled
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Skills You'll Learn

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

Reviews

4.9 (111,312 ratings)
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    86 ratings
CS

Jul 16, 2019

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).\n\nAndrew Ng is excellent

KM

Aug 11, 2017

Very nicely explained the mathematical topics, even for people like me with some phobia regarding large formulas. Useful hands-on experience with MATLAB coding, which I would have had to learn anyway.

From the lesson
Linear Regression with Multiple Variables
What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.

Taught By

  • 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

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