What is the translation of " 深度学习模型 " in English?

• deep learning models
• deep-learning model
• deep learning model
• deep-learning models

Examples of using 深度学习模型 in Chinese and their translations into English

我们通过这种方式训练深度学习模型，来提取这些原因。
In this way we train the deep-learning model to extract these rationales.

在深度学习模型中，特征由AI本身识别。
In a deep learning model, features are identified by the A.I. itself.

这些对抗性的例子经常被用来攻击深度学习模型。
These adversarial examples are often used to attack a deep learning model.

深度学习模型的输入数据可以有多个通道。
Input data to Deep Learning models can have multiple channels.

否则，深度学习模型可能无法像预期的那样准确地执行。
Otherwise, the deep learning models may not perform as accurately as expected.

深度学习模型的目标函数可能有若干局部最优值。
The objective function of the deep learning model may have several local optimums.

Lore在15分钟内建立深度学习模型.
How to build a deep learning model in 15 minutes.

Theano是一个python库，使编写深度学习模型容易，并给出了在GPU上训练他们的选择。
Theano, python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

大多数深度学习方法使用神经网络架构，这也是深度学习模型通常被称为深度神经网络的原因。
Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks.

深度学习模型已被成功地用于患者,可能导致更一致的筛选程序.
Deep-learning model has been used successfully on patients, may lead to more consistent screening procedures.

年前你不可能有深度学习模型，因为你没有数据和计算能力。
You couldn't have had a deep learning model 30 years ago, because you didn't have the data and the computing power.”.

深度学习模型已经被证明很容易受到数据中难以察觉的扰动，这些扰动会欺骗模型做出错误的预测或分类。
Deep learning models have been shown to be vulnerable to imperceptible perturbations in data that dupe models into making wrong predictions or classifications.

该深度学习模型或能推动前瞻性临床试验，提高乳腺癌筛查的准确性和效率。
This deep-learning model may contribute to prospective clinical trials to improve the accuracy and efficiency of breast cancer screening.

在本教程中，我们将构建一个Python深度学习模型，用于预测股票价格的未来行为。
In this tutorial, we will build a Python deep learning model that will predict the future behavior of stock prices.

我的腾讯AILab研究团队正在研究使用深度学习模型获取多媒体数据的复杂特征和行为。
My Tencent AI Lab research group is working to use deep-learning models to capture multimedia data's complicated characteristics and behaviors.

这种自动化的特征提取使深度学习模型能够为计算机视觉任务（如对象分类）提供高精确度。
This kind of automated feature extraction enables deep learning models to be highly accurate for computer vision tasks like object classification.

训练和运行像深度学习模型这样的东西需要处理大量的数据，因而占用内存和处理器。
Training and running things like deep-learning models involves crunching vast amounts of data, which taxes memory and processors.

论文结果显示，深度学习模型能够以资深放射科医生的水平读取扫描，并提高密度评估的一致性。
The paper's results show the deep learning model can read scans at the level of experienced radiologists and improve the consistency in their density assessments.

而深度学习模型通常需要硬件加速器，如GPU、TPU或FPGA来进行训练，以及大规模的部署。
Deep learning models often need hardware accelerators such as GPUs, TPUs, or FPGAs for training, and also for deployment at scale.

相比之下，新的深度学习模型实际上建立了基于句子结构的整个句子的表示。
In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure.

Keras是一个模型级（model-level）的库，为开发深度学习模型提供了高层次的构建模块。
Keras is a model-level library, providing high-level building blocks for developing deep-learning models.

Theano是一个Python库，使得写深度学习模型很容易，并给出了在GPU上训练的选项。
Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

Singa是一个Apache的孵化器项目，也是一个开源框架，作用是使在大规模数据集上训练深度学习模型变得更简单。
Singa, an Apache Incubator project, is an open source framework intended to make it easy to train deep-learning models on large volumes of data.

深度学习模型的准确性在很大程度上取决于训练数据的质量。
The accuracy of a deep learning model depends to a very great extent on the quality of the training data.

正确地调整深度学习模型需要巨大的数据集，图形处理单元或张量处理单元和时间。
To tune a deep learning model correctly requires immense data sets, graphic processing units or tensor processing units, and time.

GPU执行矩阵乘法必须比传统CPU更快，这意味着机器学习和深度学习模型可以执行得更快。
GPUs perform matrix multiplication must faster than traditional CPUs, which means machine learning and deep learning models can perform much faster.

自编码器是一种无监督深度学习模型，它试图将自己的输入复制到输出。
An autoencoder is an unsupervised deep learning model that attempts to copy its input to its output.

当网络训练一组图像时，它们重新学习，这使得深度学习模型对于计算机视觉任务非常准确。
They're learned while the network trains on a set of images, which makes deep learning models extremely accurate for computer vision tasks.

因此，一个可解释的深度学习的研究机会是将人类的知识结合起来，以提高深度学习模型的鲁棒性。
Accordingly, one research opportunity concerning explainable deep learning is to incorporate human knowledge to improve the robustness of deep learning models.