Visualizing the gradient descent method
Por um escritor misterioso
Last updated 29 agosto 2024
![Visualizing the gradient descent method](https://scipython.com/static/media/uploads/blog/linreg-graddesc/linreg2d.png)
In the gradient descent method of optimization, a hypothesis function, $h_\boldsymbol{\theta}(x)$, is fitted to a data set, $(x^{(i)}, y^{(i)})$ ($i=1,2,\cdots,m$) by minimizing an associated cost function, $J(\boldsymbol{\theta})$ in terms of the parameters $\boldsymbol\theta = \theta_0, \theta_1, \cdots$. The cost function describes how closely the hypothesis fits the data for a given choice of $\boldsymbol \theta$.
![Visualizing the gradient descent method](https://static.javatpoint.com/tutorial/machine-learning/images/gradient-descent-in-machine-learning1.png)
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Visualizing the gradient descent method
![Visualizing the gradient descent method](https://www.researchgate.net/publication/311458101/figure/fig2/AS:1132480878915588@1647015890347/Projected-gradient-descent-and-projected-Newtons-method-for-quadratic-optimization-under.jpg)
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