迁移学习 in English translation

• transfer learning
• migration learning

Examples of using 迁移学习 in Chinese and their translations into English

是的，有种算法叫做迁移学习，你几乎可以用任何机器学习(ML)算法，而不需要重新整理系统。
Yes there is what is called transfer learning which you can use with almost any machine learning(ML) algorithm without retraining the whole system.

要回答这些问题，有必要深入研究迁移学习理论，以了解现有的不同迁移学习方法。
To answer those questions, it is worthwhile to dig deeper into the theory of transfer learning to understand the different transfer learning approaches that exist.

更重要的是，我将概述迁移学习的最新进展，并说明它是怎么减轻数据短缺问題的。
In particular, Yang will give an overview of recent advances in transfer learning and show how it can alleviate the problems of data shortage.

接下来的两节将分别讨论迁移学习在高层级和低层级的应用方法。
The next two sections will discuss respectively a high level and low level approach to transfer learning.

当前大多数迁移学习方法通常假设我们可以从不同任务数据中抽取任务间的相似性或通用特性。
Most current transfer-learning methods assume we can centralize data from different tasks for extracting commonality among tasks.

虽然有一些方法可以减少对数据的依赖，比如迁移学习、少样本学习、无监督学习和弱监督学习。
Although there are some ways to reduce the dependence on data, such as transfer learning, less sample learning, unsupervised learning and weak supervised learning..

同时这篇文章将向您介绍迁移学习的不同方法，并为您提供一些已经预先训练过的模型的资源。
It will introduce you to the different approaches of Transfer Learning and provide you with some resources on already pre-trained models.

通过迁移学习，你可以尝试从已经学习到不同任务的模式开始，启动这个泛化过程。
With transfer learning, you attempt to kickstart this generalisation process by starting from patterns that have been learned for a different task.

而与之相比，迁移学习则允许用来训练和测试的数据集的领域、任务和分布规律有所不同。
The transfer learning, in contrast, allows the domains, tasks, and distribution used in training and testing to be different.

随着迁移学习的最新进展，我们不应忘记使用目标任务特定数据的更明确的方法。
With the recent advances in transfer learning, we should not forget more explicit ways of using target task-specific data.

需要说明的是，这份列表必然有很大的主观成分，它涵盖了主要与迁移学习和泛化（Generalization）相关的想法。
The list is necessarily subjective and covers ideas mainly related to transfer learning and generalization.

为了确定一个语言任务中的ImageNet是什幺样的，我们首先要识别出是什幺特性使得ImageNet适合迁移学习。
In order to determine what an ImageNet for language might look like, we first have to identify what makes ImageNet good for transfer learning.

迁移学习是一种机器学习（ML）技术，其中在训练一组ML问题期间获得的知识可用于训练其他相似类型的问题。
Transfer learning is a machine learning(ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

这是一种称为迁移学习的现象，即，如果一个模型已掌握某些任务，那么它在学习类似的新任务时，速度将更快，使用的训练数据也将更少。
This is thanks to migration learning: if the model has mastered similar tasks, the model can learn new tasks faster and use less training data.

迁移学习试图通过将在一个或多个源任务中学习到的知识进行迁移，将它们用在相关的目标任务中以提高其学习性能。
In transfer learning, knowledge acquired in one or more source tasks is transferred and used to improve the learning of a related target task.

多任务和迁移学习.
Multi-task and Transfer Learning.

跨语言迁移学习用口语理解.
Cross- Lingual Transfer Learning for Spoken Language Understanding“.

无监督学习、迁移学习和课程设计.
Unsupervised learning, transfer learning and curriculum design.

迁移学习在NLP的成功应用.
The successful application of transfer learning to NLP.

无监督学习、迁移学习与课程设计.
Unsupervised learning, transfer learning and curriculum design.