确切地说,根据使用数据量的大小(the amount of data),时间复杂度(time complexity)和算法的准确率(accuracy of the algorithm),梯度下降法可分为: 1.批量梯度下降法(Batch Gradient Descent, BGD); 2.随机梯度下降法(Stochastic Gradient Descent, SGD); 3.小批量梯度下降法(Mini-Batch Gradient Descent, MBGD)...
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function...【吴恩达机器学习学习笔记03】Gradient Descent 一、问题综述 我们上一节已经定义了代价函数J,现在我们下面讲讨论如何找到J的最小值,梯度下降(Gradient Descent)广泛应用于机器学习的众多领域。 首先是问题...
近端梯度下降法是众多梯度下降 (gradient descent) 方法中的一种,其英文名称为proximal gradident descent,其中,术语中的proximal一词比较耐人寻味,将proximal翻译成“近端”主要想表达"(物理上的)接近&…
[1] 李航,统计学习方法 [2] An overview of gradient descent optimization algorithms [3] Optimization Methods for Large-Scale Machine Learning
Intuition Behind Gradient DescentTo understand the gradient descent algorithm, imagine a drop of water sliding down the side of a bowl or a ball rolling down a hill. The drop and the ball tend to move in the direction of the fastest decrease until they reach the bottom. With time, they’...
using Gradient Descent can be quite costly since we are only taking a single step for one pass over the training set – thus, the larger the training set, the slower our algorithm updates the weights and the longer it may take until it converges to the global cost minimum (note that the...
Gradient descent is an optimization algorithm used to train machine learning models by minimizing errors between predicted and actual results.
计算流程 对于样本数量为m个的训练集 首先初始化参数值(对于有多个局部极值local optimum的问题 不同的初始化值会得到不同的局部极值) 即令每一个θ都为某一个值 然后利用公式 h是预测值 y是样本输出值 x是样本输入值 j是样本数 α是剃度速率 也就是控制每次收敛幅度的一个系数 ...
The gradient descent algorithm would oscillate a lot back and forth, taking a long time before finding its way to the minimum point. 1. A stretched contour plot, due to missing input feature scaling. With feature scaling we will bring back the original bowl-shaped figure in order to let ...
Gradient descent is about shrinking the prediction error or gap between the theoretical values and the observed actual values, or in machine learning, the training set, by adjusting the input weights. The algorithm calculates the gradient or change and gradually shrinks that predictive gap to refine...