What is the translation of " 模型培训 " in English?

• model training

Examples of using 模型培训 in Chinese and their translations into English

上一篇Explorium融资1,900万美元用于统一AI模型培训和部署.
Explorium raises $19 million to unify AI model training and deployment.

Explorium融资1,900万美元用于统一AI模型培训和部署.
Explorium raises $19 million to unify AI model training and deployment.

下一篇Explorium融资1,900万美元用于统一AI模型培训和部署.
Explorium raises $19 million to unify AI model training and deployment.

GrantReaber指出，TensorBoard可以轻松监控模型培训。
Grant Reaber notes that TensorBoard makes it easy to monitor model training.

当模型培训完成后，“Classificationtable”将提供有关模型健康状况的即时快照。
When model training is completed, the“Classification table” gives you an instantaneous snapshot of the model's health.

这与大规模人工智能模型培训的繁重工作是分开的，这将在集中式云计算数据中心完成。
This is separate from the heavy lifting for AI model training at scale that will be done in centralized cloud data centers.

但AWS还没有发布开发人员工具来评估模型培训其他方面的公平性。
But AWS has not yet released developer tools to address assessing fairness in other aspects of model training.

根据发布，该合作将首先关注光学字符识别（OCR），计算机视觉（CV）和模型培训。
It says the partnership will focus on optical character reader(OCR), computer vision(CV) and model training.

完整的端到端的项目，能使你实践每一个主要步骤(即数据清理、模型培训等)。
Complete end-to-end projects that allow you to practice every major step(i.e. Data Cleaning, Model Training, etc.).

百度推出EZDL，一个不需要编码经验的AI模型培训平台.
Baidu launches EZDL, an AI model training platform that requires no coding experience.

实际上，SGD是大多数深度学习框架模型培训中使用的实际优化方法。
In fact, SGD is the de facto optimization method used during model training for most of the deep learning frameworks.

定义了尝试解决的问题后，我们将对历史数据进行分析和预处理，以便为模型培训做好准备工作。
After we set the problem we are trying to solve, we analyze and pre-process historical data in preparation for model training.

模型培训现在支持除m4.large、c4.large和c5.large实例之外的几乎所有M4、M5、C4、C5、P2和P3实例。
Model training now supports nearly all M4, M5, C4, C5, P2, and P3 instances with the exception of m4. large, c4. large, and c5. large instances.

一种常见的做法是将包含处理过的数据的数据集分为两部分：一部分为模型培训预留，另一部分用于测试。
It is common practice to split the dataset containing the massaged data into two: one reserved for model training, and the other for testing.

不能直接从标准模型培训过程中的数据中学习，需要….
Cannot be learned directly from the data in the standard model training process and need to be predefined.

使用一个未用于模型培训的数据样本将允许对其精确度进行客观的评估。
Using a data sample that was not involved in the training of the model allows for a non-biased assessment of its accuracy.

这使数据科学家和模型培训师能够专注于他们的核心竞争力，而不是无休止地定制，建模和培训AI实施。
This frees data scientists and model trainers to focus on their core competencies rather than endlessly customizing, modeling, and training an AI implementation.

关于使用模型的培训方案.
Training programmes on the use of the model.

高级CPU模型也无法提高AI培训模型的速度。
Even very advanced CPUs cannot increase the velocity of AI model training.

就非能源部门如何使用模型提供培训；.
Provide training on the use of models in non-energy sectors.

高级CPU模型也无法提高AI培训模型的速度。
Even the fastest CPUs might not improve the speed of training an AI model.

高级CPU模型也无法提高AI培训模型的速度。
Even the highly-advanced CPU may not advance the speed of training an AI model.

损失函数-这可以衡量模型在培训过程中的准确程度。
Loss function- This measures how accurate the model is during training.

颅骨模型创建导航培训发展模型中使用快速原型技术的利用。
The utilization of cranial models created using rapid prototyping techniques in the development of models for navigation training.

即使是最快和最先进的CPU也可能无法提高AI模型的培训速度。
The fastest and advanced CPU might not enhance the training speed of the AI model.

即使是最快和最先进的CPU也可能无法提高AI模型的培训速度。
Even the fastest and most advanced CPU may not improve the speed of training an AI model.

该平台需要支持“数据流、实验、构建培训模型，以及部署和管理这些经过培训的模型。
The platform needs to support“the flow of data, experimentation, building training models, and deploying and managing those trained models.”.

这使数据科学家和模型培训师可以将精力集中在核心能力上，而不必担心底层AI/ML基础。
This frees data scientists and model trainers to focus on their core competencies and not worry with the underlying AI/ML foundation.

这使数据科学家和模型培训师可以将精力集中在核心能力上，而不必担心底层AI/ML基础。
It exempts data scientists and model trainers to concentrate on their core competencies and not worry with the underlying AI/ML foundation.