训练样例 英语是什么意思 - 英语翻译

• training examples

在 中文 中使用 训练样例 的示例及其翻译为 英语

每个任务下方的数字表示训练样例的数量。
The number below each task denotes the number of training examples.

典型的用例就是发现训练样例之间隐藏的结构或者关系。
The typical use case is to discover the hidden structure and relations between the training examples.

本文中描述的模型有训练样例，每个图像都有一个句子（或标题）来描述。
The model described in the paper has training examples that have a sentence(or caption) associated with each image.

通过他们的框架，机器人被给予有限数量的训练样例，并使用它们来推广到新对象。
The framework gives the robot a limited number of training examples and uses them to generalize to new objects.

例如，标准的决策树学习器不能比具有训练样例的决策树学习更多。
For example, standard decision tree learners cannot learn trees with more leaves than there are training examples.

简介机器学习问题，一般都涉及从特殊训练样例中得到一般的概念。
Much of machine learning involves acquiring general concepts from specific training examples.

幸运的是，训练数据还包括背景噪声，我们将这些噪音与各种训练样例混合在一起。
Luckily, the training data also includes background noise which we mix with our training examples at various volumes.

取决于你想要解决的问题，我们将会使用到几百，几千，几百万甚至上亿的训练样例。
Depending on the problem you want to solve, we may be talking in the order of hundreds, thousands, millions or billions of training examples.

例如，标准的决策树学习器无法学习出比训练样例更多的叶子节点。
For example, standard decision tree learners cannot learn trees with more leaves than there are training examples.

这是巨大的成本，因为你不能为每个训练样例改变所做事情。
There's a huge cost to that, which is that you cannot change what you do for each training example.

而且，随着训练样例的增加，神经网络可以从中学习到更多信息，从而提高它的准确度。
Furthermore, by increasing the number of training examples, the network can learn more about handwriting, and so improve its accuracy.

大多数机器学习系统通过梳理训练样例并寻找与人类注释器提供的分类相对应的模式来工作。
Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators.

通过这种方式，这些系统仅仅从统计训练样例中就发现了重要的关系信息。
In this way, these systems discover important relational information purely from the statistics of training examples.

它的思想是利用大量的手写数字，亦被称为训练样例，.
The idea is to take a large number of handwritten digits, known as training examples.

在《AlgebraicMind》中，我将这种特定的外推法称为在训练样例空间之外推广普遍量化的一对一映射。
In The Algebraic Mind I referred to this specific kind of extrapolation as generalizing universally quantified one-to-one mappings outside of a space of training examples.

机器学习-从特定的训练样例中形成一般规则-“不管他们使用多少训练样例，都无法保证100％正确的结果。
Machine learning- which forms general rules from specific training examples-“cannot guarantee 100% correct results, no matter how many training examples they use.”.

现在我们来看一个训练样例：.
Now we will look at a single training example:.

现在我们来看一个训练样例：.
Now let's look at a training example:.

一个类ImageNet数据集应该足够大，比如拥有数百万个训练样例。
An ImageNet-like dataset should be sufficiently large, i.e. on the order of millions of training examples.

我们使用这个方法来找到支持向量的训练样例和并作出强调。
We have used this methods here to find the training examples that are support vectors and highlight them.

这意味着我们的神经网络已经学会了正确的分类我们的第一个训练样例。
This means that our network has learnt to correctly classify our first training example.

一个在好的训练样例上得到的策略会比一个没有好的训练样例的策略能更快地引导自己。
A policy that randomly stumbles onto good training examples will bootstrap itself much faster than a policy that doesn't.

它适用于由训练样例组成的标记训练数据。
It works with labeled training data made up of training examples.

在训练集上表现出色其实很简单（只要记住这些训练样例即可）。
Doing well on the training set is easy(just memorize the examples).

该系统使用上千个视频样例进行训练，视频带有鼓棍敲打不同表面产生的不同声音。
The system is trained using 1000 examples of video with sound of a drumstick striking different surfaces and creating different sounds.

我们在数据集中重复这个过程和所有其他的训练样例。
We repeat this process with all other training examples in our dataset.

首先神经网络开始为自己分配一些随机权，此后，它使用训练样例训练自身。
First the neural network assigned itself random weights, then trained itself using the training set.

现在你的过滤器阻止了至少74％的反面样例，这些样例可以成为训练数据。
Now your filter is blocking at least 74% of the negative examples.

诚然，每个样例都需要训练一个收敛的神经网络，但这仍然是非常有效的。
Admittedly, each example required training a neural net to convergence, but this is still very sample efficient.