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Reviewing Stanford on Linear Regression and Gradient Descent

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Indhold leveret af Brian Carter. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Brian Carter eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

This lecture from Stanford University's CS229 course, "Machine Learning," focuses on the theory and practice of linear regression and gradient descent, two fundamental machine learning algorithms. The lecture begins by motivating linear regression as a simple supervised learning algorithm for regression problems where the goal is to predict a continuous output based on a set of input features. The lecture then introduces the cost function used in linear regression, which measures the squared error between the predicted output and the true output. Gradient descent, an iterative algorithm, is then explained as a method to find the parameters that minimize the cost function. Two variants of gradient descent, batch gradient descent and stochastic gradient descent, are discussed with their respective strengths and weaknesses. The lecture concludes with a derivation of the normal equations, an alternative approach to finding the optimal parameters in linear regression that involves solving a system of equations rather than iteratively updating parameters.

Watch Andrew Ng teach it at Stanford: https://www.youtube.com/watch?v=4b4MUYve_U8&t=1086s&pp=ygUSdmFuaXNoaW5nIGdyYWRpZW50

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61 episoder

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iconDel
 
Manage episode 446880554 series 3605861
Indhold leveret af Brian Carter. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Brian Carter eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

This lecture from Stanford University's CS229 course, "Machine Learning," focuses on the theory and practice of linear regression and gradient descent, two fundamental machine learning algorithms. The lecture begins by motivating linear regression as a simple supervised learning algorithm for regression problems where the goal is to predict a continuous output based on a set of input features. The lecture then introduces the cost function used in linear regression, which measures the squared error between the predicted output and the true output. Gradient descent, an iterative algorithm, is then explained as a method to find the parameters that minimize the cost function. Two variants of gradient descent, batch gradient descent and stochastic gradient descent, are discussed with their respective strengths and weaknesses. The lecture concludes with a derivation of the normal equations, an alternative approach to finding the optimal parameters in linear regression that involves solving a system of equations rather than iteratively updating parameters.

Watch Andrew Ng teach it at Stanford: https://www.youtube.com/watch?v=4b4MUYve_U8&t=1086s&pp=ygUSdmFuaXNoaW5nIGdyYWRpZW50

  continue reading

61 episoder

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