Visualizing the gradient descent method
Por um escritor misterioso
Descrição
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 $\boldsymbol \theta$.
Understanding Gradient Descent. Introduction, by Necati Demir
Gradient-Based Optimizers in Deep Learning - Analytics Vidhya
How to visualize Gradient Descent using Contour plot in Python
Subgradient Descent Explained, Step by Step
A journey into Optimization algorithms for Deep Neural Networks
Gradient descent visualization - hills
Visualizing the gradient descent in R · Snow of London
How to implement a gradient descent in Python to find a local minimum ? - GeeksforGeeks
4. A Beginner's Guide to Gradient Descent in Machine Learning, by Yennhi95zz
de
por adulto (o preço varia de acordo com o tamanho do grupo)
