数学代写|matlab代写|BMS13

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

statistics-lab™ 为您的留学生涯保驾护航 在代写matlab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matlab代写方面经验极为丰富，各种代写matlab相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

数学代写|matlab代写|Applications of Deep Learning

Deep learning is used in many applications today. Here are a few:
Image recognition – This is arguably the best known and most controversial use of deep learning. A deep learning system is trained with pictures of people. Cameras are distributed everywhere, and images are captured. The system then identifies individual faces and matches them against its trained database. Even with variations in lighting, weather conditions, and clothing, the system can identify the people in the images.

Speech recognition – You hardly ever get a human being on the phone anymore. You are first presented with a robotic listener that can identify what you are saying, at least within the limited context of what it expects. When a human listens to another human, the listener is not just recording the speech, they are guessing what the person is going to say and filling in gaps of garbled words and confusing grammar. Robotic listeners have some of the same abilities. A robotic listener is an embodiment of the “Turing test.” Did you ever get one that you thought was a human being? Or for that matter, did you ever reach a human who you thought was a robot?

Handwriting analysis – A long time ago, you would get forms in which you had boxes in which to write numbers and letters. At first, they had to be block capitals! A robotic handwriting system could figure out the letters in those boxes reliably. Years later, though many years ago, the US Post Office introduced zip code reading systems. At first, you had to put the zip code on a specific part of the envelope. That system has evolved so that it can find zip codes anywhere. This made the zip $+4$ system valuable and a big productivity boost.

Machine translation – Google translate does a pretty good job considering it can translate almost any language in the world. It is an example of a system with online training. You see that when you type in a phrase and the translation has a checkmark next to it because a human being has indicated that it is correct. Figure $1.10$ gives an example. Google harnesses the services of free human translators to improve its product!

Targeting – By targeting, we mean figuring out what you want. This may be a movie, a clothing item, or a book. Deep learning systems collect information on what you like and decide what you would be most interested in buying. Figure $1.11$ gives an example. This is from a couple of years ago. Perhaps, ballet dancers like Star Wars!

Other applications include game playing, autonomous driving, medicine, and many others. Just about any human activity can be an application of deep learning.

数学代写|matlab代写|Organization of the Book

This book is organized around specific deep learning examples. You can jump into any chapter as they are pretty much independent. We’ve tried to present a wide range of topics, some of which, hopefully, align with your work or interests. The next chapter gives an overview of MATLAB products for deep learning. Besides the core MATLAB development environment, we only use three of their toolboxes in this book.
Each chapter except for this and the next is organized in the following order:

1. Modeling
2. Building the system
3. Training the system
4. Testing the system
Training and testing are often in the same script. Modeling varies with each chapter. For physical problems, we derive numerical models, usually sets of differential equations, and build simulations of the processes.

The chapters in this book present a range of relatively simple examples to help you learn more about deep learning and its applications. It will also help you learn the limitations of deep learning and areas for future research. All use the MATLAB Deep Learning Toolbox.

1. What Is Deep Learning? (this chapter).
2. MATLAB Machine Learning Toolboxes – This chapter gives you an introduction to MATLAB machine intelligence toolboxes. We’ll be using three of the toolboxes in this book.
3. Finding Circles with Deep Learning – This is an elementary example. The system will try to figure out if a figure is a circle. It will be presented with circles, ellipses, and other objects and trained to determine which are circles.
4. Classifying Movies – All movie databases try to guess what movies will be of most interest to their viewers to speed movie selection and reduce the number of disgruntled customers. This example creates a movie rating system and attempts to classify movies in the movie database as good or bad.
5. Algorithmic Deep Learning – This is an example of fault detection using a detection filter as an element of the deep learning system. It uses a custom deep learning algorithm, the only example that does not use the MATLAB Deep Learning Toolbox.
6. Tokamak Disruption Detection – Disruptions are a major problem with a nuclear fusion device known as a Tokamak. Researchers are using neural nets to detect disruptions before they happen so that they can be stopped. In this example, we use a simplified dynamical model to demonstrate deep learning.

数学代写|matlab代写|Organization of the Book

1. 造型
2. 构建系统
3. 训练系统
4. 测试系统
训练和测试通常在同一个脚本中。建模因每一章而异。对于物理问题，我们推导出数值模型，通常是微分方程组，并建立过程模拟。

1. 什么是深度学习？（本章）。
2. MATLAB 机器学习工具箱——本章介绍 MATLAB 机器智能工具箱。我们将使用本书中的三个工具箱。
3. Finding Circles with Deep Learning——这是一个基本的例子。系统将尝试判断图形是否为圆形。它将与圆圈、椭圆和其他对象一起呈现，并接受训练以确定哪些是圆圈。
4. 对电影进行分类——所有电影数据库都试图猜测观众最感兴趣的电影是什么，以加快电影选择速度并减少不满客户的数量。此示例创建一个电影评级系统，并尝试将电影数据库中的电影分类为好或坏。
5. 算法深度学习——这是一个使用检测过滤器作为深度学习系统元素的故障检测示例。它使用自定义深度学习算法，这是唯一不使用 MATLAB 深度学习工具箱的示例。
6. 托卡马克中断检测——中断是称为托卡马克的核聚变装置的主要问题。研究人员正在使用神经网络在中断发生之前检测它们，以便可以阻止它们。在此示例中，我们使用简化的动力学模型来演示深度学习。

有限元方法代写

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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。