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 $\boldsymbol \theta$.
A Visual Explanation of Gradient Descent Methods (Momentum, AdaGrad, RMSProp, Adam), by Lili Jiang
Variants of Gradient Descent Algorithm
Why Visualize Gradient Descent Optimization Algorithms ?, by ASHISH RANA
Gradient-Based Optimizers in Deep Learning - Analytics Vidhya
Visualize various gradient descent algorithms
Simplistic Visualization on How Gradient Descent works
Stochastic Gradient Descent (SGD): A New Way to Visualize This Beauty, by Ketan Suhaas Saichandran
Visualizing the gradient descent method
How to visualize Gradient Descent using Contour plot in Python
Why Visualize Gradient Descent Optimization Algorithms ?, by ASHISH RANA
Gradient descent.
Guide to Gradient Descent Algorithm: A Comprehensive implementation in Python - Machine Learning Space
Reducing Loss: Gradient Descent, Machine Learning
Gradient descent visualization - hills
Visualizing Newton's Method for Optimization II
de
por adulto (o preço varia de acordo com o tamanho do grupo)