n most situations, a single cost function will not be sufficient to produce complex vehicle behavior. In this quiz, we'd like you to implement one more cost function in C++. We will use these two C++ cost functions later in the lesson. The goal with…

In the previous quizzes, you designed a cost function to choose a lane when trying to reach a goal in highway driving: cost=1−e​−​​∣Δd∣​​​​/​Δs Here, Δ was the lateral distance between the goal lane and the final chosen lane, and Δ was the longitudin…

A  key part of getting transitions to happen when we want  them to is the design of reasonable cost functions. we want to penalize and reward the right things. I am going to work through an example of  one way you  might think about designing  a cost…

Designing cost functions is difficult and getting them all to cooperate to produce reasionable vehicle behavior is hard. some of the difficulties associated with cost function design include solving new problems without unsolving old ones. when you a…

  In the image above, the blue self driving car (bottom left) is trying to get to the goal (gold star). It's currently in the correct lane but the green car is going very slowly, so it considers whether it should perform a lane change (LC) or just ke…

其它的比gradient descent快, 在某些场合得到广泛应用的求cost function的最小值的方法 when have a large machine learning problem,一般会使用这些advanced optimization algorithm而不是gradient descent Conjugate gradient, BFGS,L-BFGS很复杂,可以在不明白详细原理的情况下进行应用(使用software libary). 可以使用Octave和matlab的…

One way to implement a transition function is by generating rough trajectories for each accessible "next state" and then finding the best. To "find the best" we generally use cost functions. We can then figure out how costly each rough…

实际上,代价函数(cost function)和损失函数(loss function 亦称为 error function)是同义的.它们都是事先定义一个假设函数(hypothesis),通过训练集由算法找出一个最优拟合,即通过使的cost function值最小(如通过梯度下降),从而估计出假设函数的未知变量. 例如: 可以看做一个假设函数,而与之对应的loss function如下: 通过使E(w)值最小,来估计出相应的w值,从而确定出假设函数(目标函数),实现最优拟合. 硬要说区别的话,l…

@tags: caffe 机器学习 在机器学习(暂时限定有监督学习)中,常见的算法大都可以划分为两个部分来理解它 一个是它的Hypothesis function,也就是你用一个函数f,来拟合任意一个输入x,让预测值t(t=f(x))来拟合真实值y 另一个是它的cost function,也就是你用一个函数E,来表示样本总体的误差. 而有时候还会出现loss function,感觉会和cost function混淆. 上quora看了下,有个同名问题,回答的人不多,upvote更少..回答者里面…

逻辑回归模型预估的是样本属于某个分类的概率,其损失函数(Cost Function)可以像线型回归那样,以均方差来表示:也可以用对数.概率等方法.损失函数本质上是衡量”模型预估值“到“实际值”的距离,选取好的“距离”单位,可以让模型更加准确. 1. 均方差距离 \[{J_{sqrt}}\left( w \right) = {\sum\limits_{i = 1}^m {{y_i}\left( {1 - p\left( {{x_i};w} \right)} \right)} ^2} + \left…