视频:训练AI的最佳实践
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阅读这段录音的完整文本:
卡伦加拉格尔: 在商业和技术方面有一种推拉关系. 在商业方面, 你要经常倾听客户的意见, 问他们想要什么, 实现这些东西. 但这对技术方面有影响,你必须考虑到这一点. So 例如, we had a hockey customer that said, "Hey, can you pull out all the face-offs?我们说:“当然可以。.“所以我们教了一个分类器如何从视频中理解, 没有元数据, 当对峙发生时, 把它们剪掉.
当你改变模型时,这是其中一件事, 它开始在神经网络中做其他事情, 从技术的角度来看,在整个过程中你需要担心. 对我们来说, 我们遇到了模型过拟合的问题, 所以现在它可以在成千上万款不同的游戏中查看, 但记分牌, 现在我们已经实现了这个新模块, 它开始只关注它所知道的那种记分牌, 变得僵硬和僵硬.
当你在制定你的人工智能战略并使用最佳实践之类的东西时, just understand the push and the pull between the product requirements and the business side stuff, 它在技术方面的作用, 以及从长远来看它将如何影响你的产品.
Josh灰色: 好吧, 我想补充一点, that overfitting problem is an incredibly common first result when you're going through some of these exercises. And a lot of that is related to the selection of your training dataset relative to your output. 如果你想找记分牌的话, 你训练了一堆图片, 他们种类繁多,但记分牌总是很突出, then when you start throwing real images where maybe the scoreboard is a little more off to the side, 或者不同的角度, 您可能会发现您过度拟合了一个非常干净的计分板分类器. 当你明白你想从中得到什么, 确保将正确的数据集放入其中, 确保你能得到你想要的食物种类.
杰森·霍夫曼: 在你选择了你想要用来训练它的数据之后, 还有一些关于如何训练算法的最佳实践, 就像, 例如, 使用训练数据的随机子集,而不是所有的训练数据, 或者使用五个随机重叠的数据子集来训练它, 然后用一些它从未见过的数据来测试它. 所以很多工作台上都有最佳实践, 单击工作台. 有很多, 比如从MATLAB中, 其他人, 他们中的很多人会指导你, 然后说, “我知道你有一百万个数据点. 别把这些都教我. 我们做15次10的迭代,每000个数据点, 看看我们能想出什么, 然后我们来看看它在100的情况下是怎样的,我从未见过的1000个数据点."
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