What is the translation of " LEARNING PROBLEMS " in Chinese?

• 学习问题

Examples of using Learning problems in English and their translations into Chinese

Learning problems and attention deficits.
学习困难与注意缺陷。

Refresher courses for teachers of pupils with learning problems.
教授有学习问题儿童的教师的复习课程.

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

Since much of the training data is also relational, this type of data structure would seem ideally suited to machine learning problems.
由于大部分训练数据也是关系型的，这种类型的数据结构似乎非常适合于机器学习问题。

I have said it before, working machine learning problems is addictive.
我之前说过，处理机器学习的问题是会让人上瘾的。

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

Upon completion, you will be able to solve deep learning problems that require multiple types of data inputs.
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。

As learning problems grow in scale and complexity, and expand into multi-disciplinary territory, a more modular approach for scaling ANNs will be needed.
随着学习问题在规模和复杂性方面的增长，并扩展到多学科领域，将需要用于缩放人工神经网络的模块化方法。

Once the course is over, it will be possible to solve Deep Learning problems that require different data inputs.
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。

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

Once we have defined the business problem and decomposed into machine learning problems, we need to dive deeper into the data.
一旦我们定义了业务问题，并且分解成了机器学习问题，我们接下来需要深入了解数据。

In most Supervised Machine Learning problems we need to define a model and estimate its parameters based on a training dataset.
在大多数监督性机器学习问题中，我们需要定义一个模型并基于训练数据集预估其参数。

A lack of such ambitious collaborations has been a‘rate-limiting factor' in tackling reading and learning problems, which are major social issues.
缺乏这样的雄心勃勃的合作一直在解决读书和学习上的问题，这是重大的社会问题了‘限速因素'。

MLlib currently supports 4 common machine learning problems: classification, regression, clustering, and collaborative filtering.
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。

Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.
其中训练数据包括输入矢量的示例以及它们对应的目标矢量的应用被称为监督学习问题。

Here, based on our specific machine learning problems, we apply useful algorithms like regressions, decision trees, random forests, etc.
这里，基于具体的机器学习问题，我们要应用有效的算法，如回归，决策树，随机森林等。

We originally designed Ludwig as a generic tool for simplifying the model development and comparison process when dealing with new applied machine learning problems.
Uber最初将Ludwig设计为通用工具，用于在处理新的应用机器学习问题时简化模型开发和比较过程。

After a brief overview of different machine learning problems, we discuss linear regression, its objective function and closed-form solution.
在简要地概述各种机器学习问题后，我们讨论线性回归，它的目标函数和闭合解。

Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems.
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。

Many types of machine learning problems require time series analysis, including classification, clustering, forecasting, and anomaly detection.
很多类型的机器学习问题都需要时间序列分析，其中包括分类、聚类、预测和异常检测。

The Learning Assistance Program is available to students who have diagnosed learning differences or who are experiencing learning problems and may need educational assistance.
学习援助方案是提供给谁已确诊的学习差异或谁遇到学习问题，可能需要教育援助的学生。

Note that in supervised learning problems such as regression(and classification), we are not seeking to model the distribution of the input variables.
注意，在监督学习问题如回归（或分类）中，我们不是为了寻找输入变量分布的模型。

Females with HSD10 disease may have developmental delay, learning problems, or intellectual disability, but they do not experience developmental regression.
HSD10缺乏女性可能有发育迟缓，学习问题，或智力残疾，但他们没有过发育倒退的经历。

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.
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。

SGD has been successfully applied to large-scale and sparse machine learning problems often encountered in text classification and natural language processing.
SGD已成功应用于文本分类和自然语言处理中经常遇到的大规模和稀疏机器学习问题。

NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems..
NET来解决许多不同类型的机器学习问题，包括分类、推荐或集群等标准问题与针对特定领域问题的定制解决方案。

We will learn about the various unsupervised machine learning problems and as well as the appropriate algorithm to use for each problem type.
我们将学习各种各样的非监督式学习问题，以及针对每种问题类型使用的适当算法。

Apply mathematical concepts regarding the most common machine learning problems, including the concept of learnability and some elements of information theory.
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。

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples.
机器学习也与优化密切相关：许多学习问题被公式化为训练集的一些损失函数的最小化。

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples.
机器学习与优化也有着密切的联系：许多学习问题被描述为训练集上的一些损失函数的最小化。