Examples of using Gradient descent in English and their translations into Chinese
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An intuitive way to think of Gradient Descent is to imagine the path of a river originating from top of a mountain.
想想渐变下降的一种直观的方式是想象一条源于山顶的河流的小路。Now we will take the concept of computation graphs and gradient descent together and see how the parameters of logistic regression can be updated.
The second method optimized scores through gradient descent for building highly accurate structures.
第二种方法是通过梯度下降来优化评分，得到的结构高度精确。Specifically, let's consider the gradient descent algorithm, which starts with some initial θ, and repeatedly performs the update.
特别地，让我们考虑梯度下降算法，以某个初始的θ开始，重复执行更新：.If we define the batch size to be 1, this is called stochastic gradient descent.
如果mini-batch的大小为1，这叫做随机梯度下降法。This can make SGD faster than Batch Gradient Descent, depending on the problem.
这可能会使SGD比批量渐变下降更快，具体取决于问题。An intuitive way to think of Gradient Descent is to imagine the path of a river originating from top of a mountain.
梯度下降的一个直观理解就是想象一条源自山顶的河流。Now it's time to put all of this together and use gradient descent to make our dither pattern better.
现在是把所有这些放在一起的时候了，并且使用梯度下降来使抖动模式更好。Let's examine a better mechanism- very popular in machine learning- called gradient descent.
我们来研究一种更好的机制，这种机制在机器学习领域非常热门，称为梯度下降法。You can think of this update rule as defining the gradient descent algorithm.
你可以把这个更新规则看作梯度下降算法的定义。The houses in the gradient descent district are all smooth curves and densely intertwined patterns, almost more like a jungle than a city.
梯度下降区域的房子都是平滑曲线以及密切交织在一起的模式，看起来几乎更像一个丛林，而不是一座城市。And like many other things in machine learning, we can learn attribute weights by gradient descent.
和机器学习中的许多事情一样，我们可以通过梯度下降来掌握属性权值。This tutorial assumes a basic knowledge of machine learning(specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent).
本教程假定您已经有了基本的机器学习知识（具体而言，熟悉监督学习，逻辑斯特回归以及梯度下降法的思想）。It is called gradient boosting because it uses a gradient descent algorithm to minimize the loss when adding new models.
之所以称为Gradient，是因为在添加新模型时使用了梯度下降算法来最小化的损失。Up to this point, we have seen how to use gradient descent for updating the parameters for logistic regression.
到目前为止，我们已经看到了如何使用梯度下降来更新Logistic回归的参数。Actually, it turns out that while neural networks are sometimes intimidating structures, the mechanism for making them work is surprisingly simple: stochastic gradient descent.
实际上，事实证明，虽然神经网络有时是令人畏惧的结构，但使它们工作的机制出奇地简单：随机梯度下降。I usually apply that same method to logistic regression, too to monitor a gradient descent, to make sure it's converging correctly.
降法以确保其收敛，我通常也把同样的方法用在逻辑回归中，来监测梯度下降，以确保它正.However, it still serves as a decent pedagogical tool to get some of the most important ideas about gradient descent across the board.
然而，它仍然是一个不错的教学工具，有助于全面了解关于梯度下降的一些最重要的想法。This is the go-to algorithm when training a neural network and it is the most common type of gradient descent within deep learning.
请注意，它是在训练神经网络时的go-to算法，它是深度学习中最常见的梯度下降类型。Although the GQN training objective is intractable, owing to the presence of latent variables, we can employ variational approximations and optimize with stochastic gradient descent.
虽然GQN的训练十分困难，由于隐变量z的存在，我们可以借助变分推理，并借助SGD（随机梯度下降）进行优化。
