DEEP LEARNING MODEL - 翻译成中文

• 深度学习模型
• 深度学习模式

在 英语 中使用 Deep learning model 的示例及其翻译为 中文

On top of that, the deep learning model spotted 6,000 new craters in just a few hours.
最严重的是,在短短几个小时内,深层的学习模式发现了6000个新的陨石坑。

As a result, a deep learning model trained on human-annotated datasets will also miss these smaller fractures.
结果，在带有人类注释的数据集上训练的深度学习模型也将错过这些较细裂缝。

In this article, we saw how to deploy a trained Keras deep learning model as a web application.
在本文中，我们看到了如何将训练好的Keras深度学习模型部署为web应用程序。

Unlike people, the deep learning model trained to play StarCraft will not be able to play another similar game: say, in WarCraft.
与人类不同，经过训练可以玩《星际争霸》的深度学习模型将无法玩类似的游戏：例如《魔兽争霸》。

We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures.
我们将实现这个深度学习模式，以在一组照片中辨识出一只猫或是狗。

Training a deep learning model can take a long time, from days to weeks.
训练一个深刻的学习模式可能需要几个星期甚至几个月的时间。

To demonstrate it they trained a deep learning model to recognize handwritten numerals.
为了证明这一点，他们训练了一个深入的学习模型来识别手写数字。

We can tackle the challenge of customizing listening preferences by training a regression/deep learning model.
我们可以通过训练一个回归/深入学习模型来应对定制听力偏好的挑战。

This sample project uses a deep learning model generated with the TensorFlow framework to accurately detect the orientation of a person's head.
此示例项目使用TensorFlow框架生成的深度学习模型来准确检测人头部的方向。

To demonstrate it they trained a deep learning model to recognize handwritten numerals.
为了证明这一点，他们训练了一个深度学习模型来识别写手写数字。

To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach.
要做到这一点，你将需要使用正确的深度学习模型，即基于机率方法的模型。

When you train a deep learning model, two main operations are performed.
当您训练一个深度学习模型时，将执行两个主要操作：.

The team's deep learning model parses 3D volumetric data to determine the probability that there's a fault at every pixel within the image.
Fomel团队的深度学习模型会解析3D体数据，以确定图像中每个像素出现故障的可能性。

But any deep learning model needs a lot of data to train the AI in recognizing related patterns.
但所有的深度学习模型都需要大量的数据来训练AI识别相关的模式。

A Deep Learning model(such as a Convolutional Neural Network) does not try to understand the entire problem at once.
一个深度学习模型（如卷积神经网络）并不会试图即刻理解整个问题所在。

In this tutorial, you will build a deep learning model that will predict the probability of an employee leaving a company.
在本教程中，您将构建一个深度学习模型，用于预测员工离开公司的可能性。

What we want is to deploy our deep learning model as a web application accessible to anyone in the world.
因此，我们想要做的是，将深度学习模型部署成一个任何人都可以访问的web应用程序。

Once you choose and fit a final deep learning model in Keras, you can use it to make predictions on new data instances.
当你在Keras中选择好最合适的深度学习模型，就可以用它在新的数据实例上做预测了。

On its surface, a large dataset is obtained and a Deep Learning model is chosen.
首先在表层上，我们有一个巨大的数据集，并选定了一种深度学习模型。

We will even go as far as saying that you will create the Deep Learning model closest to“Artificial Intelligence”.
我们甚至会说，你将创建最接近“人工智能”的深度学习模型。