### 机器学习代写|tensorflow代写|Unsupervised learning

TensorFlow是一个用于机器学习和人工智能的免费和开源的软件库。它可以用于一系列的任务，但特别关注深度神经网络的训练和推理。

statistics-lab™ 为您的留学生涯保驾护航 在代写tensorflow方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写tensorflow代写方面经验极为丰富，各种代写tensorflow相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 机器学习代写|tensorflow代写|Unsupervised learning

Unsupervised learning is about modeling data that comes without corresponding labels or responses. The fact that we can make any conclusions at all on raw data feels like magic. With enough data, it may be possible to find patterns and structure. Two of the most powerful tools that machine-learning practitioners use to learn from data alone are clustering and dimensionality reduction.

Clustering is the process of splitting the data into individual buckets of similar items. In a sense, clustering is like classifying data without knowing any corresponding labels. When organizing your books on three shelves, for example, you likely place similar genres together, or maybe you group them by the authors’ last names. You might have a Stephen King section, another for textbooks, and a third for anything else. You don’t care that all the books are separated by the same feature, only that each book has something unique that allows you to organize it into one of several roughly equal, easily identifiable groups. One of the most popular clustering algorithms is $k$-means, which is a specific instance of a more powerful technique called the E-M algorithm.

Dimensionality reduction is about manipulating the data to view it from a much simpler perspective-the ML equivalent of the phrase “Keep it simple, stupid.” By getting rid of redundant features, for example, we can explain the same data in a lowerdimensional space and see which features matter. This simplification also helps in data visualization or preprocessing for performance efficiency. One of the earliest algorithms is principle component analysis (PCA), and a newer one is autoencoders, which are covered in chapter 7 .

## 机器学习代写|tensorflow代写|Reinforcement learning

Supervised and unsupervised learning seem to suggest that the existence of a teacher is all or nothing. But in one well-studied branch of machine learning, the environment acts as a teacher, providing hints as opposed to definite answers. The learning system receives feedback on its actions, with no concrete promise that it’s progressing in the right direction, which might be to solve a maze or accomplish an explicit goal.

Unlike supervised learning, in which training data is conveniently labeled by a “teacher,” reinforcement learning trains on information gathered by observing how the environment reacts to actions. Reinforcement learning is a type of machine learning that interacts with the environment to learn which combination of actions yields the most favorable results. Because we’re already anthropomorphizing algorithms by using the words environment and action, scholars typically refer to the system as an autonomous agent. Therefore, this type of machine learning naturally manifests itself in the domain of robotics.

To reason about agents in the environment, we introduce two new concepts: states and actions. The status of the world frozen at a particular time is called a state. An agent may perform one of many actions to change the current state. To drive an agent to perform actions, each state yields a corresponding reward. An agent eventually discovers the expected total reward of each state, called the value of a state.

Like any other machine-learning system, performance improves with more data. In this case, the data is a history of experiences. In reinforcement learning, we don’t know the final cost or reward of a series of actions until that series is executed. These situations render traditional supervised learning ineffective, because we don’t know exactly which action in the history of action sequences is to blame for ending up in a low-value state. The only information an agent knows for certain is the cost of a series of actions that it has already taken, which is incomplete. The agent’s goal is to find a sequence of actions that maximizes rewards. If you’re more interested in this subject, you may want to check out another topical book in the Manning Publications family: Grokking Deep Reinforcement Learning, by Miguel Morales (Manning, 2020; https://www .manning.com/books/grokking-deep-reinforcement-learning).

## 机器学习代写|tensorflow代写|Meta-learning

Relatively recently, a new area of machine learning called meta-learning has emerged. The idea is simple. Data scientists and ML experts spend a tremendous amount of time executing the steps of ML, as shown in figure 1.7. What if those steps-defining and representing the problem, choosing a model, testing the model, and evaluating the model-could themselves be automated? Instead of being limited to exploring only one or a small group of models, why not have the program itself try all the models?

Many businesses separate the roles of the domain expert (refer to the doctor in figure 1.7), the data scientist (the person modeling the data and potentially extracting or choosing features that are important, such as the image RGB pixels), and the ML engineer (responsible for tuning, testing, and deploying the model), as shown in figure $1.10 \mathrm{a}$. As you’ll remember from earlier in the chapter, these roles interact in three basic areas: data cleaning and prep, which both the domain expert and data scientist may help with; feature and model selection, mainly a data-scientist job with a little help from the ML engineer; and then train, test, and evaluate, mostly the job of the ML engineer with a little help from the data scientist. We’ve added a new wrinkle: taking our model and deploying it, which is what happens in the real world and is something that brings its own set of challenges. This scenario is one reason why you are reading the second edition of this book; it’s covered in chapter 2, where I discuss deploying and using TensorFlow.

What if instead of having data scientists and ML engineers pick models, train, evaluate, and tune them, we could have the system automatically search over the space of possible models, and try them all? This approach overcomes limiting your overall ML experience to a small number of possible solutions wherein you’ll likely choose the first one that performs reasonably. But what if the system could figure out which models are best and how to tune the models automatically? That’s precisely what you see in figure $1.10 \mathrm{~b}$ : the process of meta-learning, or AutoML.

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。