What is the translation of " 的学习问题 " in English?

• learning problem
• learning problems
• their learning issues

Examples of using 的学习问题 in Chinese and their translations into English

我们将使用一个简单的序列学习问题来演示TimeDistributed层。
We will use a simple sequence learning problem to demonstrate the TimeDistributed layer.

然而，并不是所有的机器学习问题都必须从头开始解决。
Not every machine learning problem has to be solved from scratch, however.

我们可以分开几个大类的学习问题：.
We can separate learning problems in a few large categories:.

如何制定基本的强化学习问题？?
How to formulate a basic reinforcement Learning problem?

本文将DQN应用在连续行动空间的强化学习问题上。
DDPG can solve the reinforcement learning problem in continuous action space.

因为没有足够的数据去解决端到端的学习问题，.
So because you don't have enough data to solve this end-to-end learning problem.

这个常见的学习问题会影响阅读能力。
This common learning issue impacts the ability to read.

再次是儿子的学习问题。
My son's learning issues.

学习有关解决基于分类的机器学习问题的基础知识，并对当前最受欢迎的一些算法进行比较研究。
Explore the basics of solving a classification-based machine learning problem, and get a comparative study of some of the current most popular algorithms.

各组之间的会议，研究学生的学习问题，共享成果，在该组的下一次会议。
Between each group meeting, learners research their learning issues and share results at the next meeting of the group.

受监督的学习问题分为“回归”和“分类”问题。
Supervised learning problems are categorized into"regression" and"classification" problems..

各组之间的会议，研究学生的学习问题，共享成果，在该组的下一次会议。
Between meetings of the group, learners research their learning issues and share results at the next meeting of the group.

矩阵完成是一个基本的机器学习问题，具有广泛的应用，特别是在协同过滤和推荐系统中。
Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems.

涉及序列的最简单的机器学习问题是一对一问题。
The simplest machine learning problem involving a sequence is a one to one problem..

第2章解释了关于最常见的机器学习问题的数学概念，包括可学习性的概念和信息论的一些内容。
Apply mathematical concepts regarding the most common machine learning problems, including the concept of learnability and some elements of information theory.

不同于传统的机器学习问题中每个数据均为向量，而在MI学习中，每个数据是一组或多组无序点。
Unlike conventional Machine Learning problems where each datum is a vector, in MI learning each datum is a point pattern or multi-set of unordered points.

这是一个具有挑战性的学习问题，它的解决方法基于强化学习的三个一般思路:.
This is a challenging learning problem, and its solution is based on three general ideas for reinforcement learning:..

更自然的学习问题也可以被视为半监督学习的实例。
More natural learning problems may also be viewed as instances of semi-supervised learning..

热门的深度学习问题就包含机器翻译：在源语言中摄取句子，并预测另一种语言的翻译。
One popular deep learning problem is machine translation: the task of ingesting sentences in some source language and predicting their translation in another language.

MLlib目前支持4种常见的机器学习问题:分类、回归、聚类和协同过滤。
MLlib currently supports 4 common machine learning problems: classification, regression, clustering, and collaborative filtering.

不幸的是，我们不能总是判断一个给定的学习问题是否可以实现，因为真正的函数是未知的。
Unfortunately, we cannot always tell whether a given learning problem is realizable, because the true function is not known.

我们将使用一个有效编码来表示LSTM的学习问题。
We will use a one hot encoding to represent the learning problem for the LSTM.

完成本课程后，您将能够解决需要多种类型数据输入的深度学习问题。
Once the course is over, it will be possible to solve Deep Learning problems that require different data inputs.

加州理工学院的研究人员开发了一种由DNA制成的人工神经网络，可以解决一个经典的机器学习问题：正确识别手写数字。
Researchers have developed an artificial neural network made out of DNA that can solve a classic machine learning problem: correctly identifying handwritten numbers.

深度学习是一个令人着迷的研究领域，而且这些技术在一系列具有挑战性的机器学习问题中取得了世界级的成果。
Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems.

完成本课程后，您将能够解决需要多种数据输入类型的深度学习问题。
Upon completion, you will be able to solve deep learning problems that require multiple types of data inputs.

在这篇文章中，我想和大家分享我的机器学习问题过程的框架。
In this post, I want to share with you the skeleton of my process for working a machine learning problem.

婴儿出生时体重不到5斤可以在生命的早期有更多的健康问题，后来在学校的学习问题。
Babies born weighing less than 5 pounds may have more health problems early in life and learning problems later in school.

如果您有未标记的数据并且想要查找结构，那么这是一个无监督的学习问题。
If we have unlabeled data and want to find structure, it's an unsupervised learning problem.