### 计算机代写|深度学习代写deep learning代考|COMP30027

statistics-lab™ 为您的留学生涯保驾护航 在代写深度学习deep learning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。我们的专家在代写深度学习deep learning代写方面经验极为丰富，各种代写深度学习deep learning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

## 计算机代写|深度学习代写deep learning代考|Optimizing the Network Architecture

As a network becomes more sophisticated with the addition of layers or various node types it puts direct consequences on how the loss/error is backpropagated through it. Figure $1.2$ demonstrates the more common problems we typically encounter when growing more complex and larger DL systems.

Larger networks mean the amount of loss needs to be divided into smaller and smaller components that eventually approach or get close to zero. When these loss components or gradients approach zero we call this a vanishing gradient problem often associated with deep networks. Conversely, components may also get exceptionally large by successively passing through layers that magnify those input signals. Resulting in gradient components getting large or what’s called exploding gradients.

Both gradient problems can be resolved using various techniques like normalizing input data and again through the layers. Special types of layer functions called normalization and dropout are shown in Figure 1.3. These techniques also add to the computational complexity and requirements for the network. They may also overtly smooth over important and characteristic features in data. Thus, requiring larger and more diverse training datasets to develop good network performance.

Normalization may solve the vanishing/exploding gradient problems of deep networks but as models grow these manifest other concerns. As networks grow, they increase the ability to digest larger sets of input, bigger images for example. Yet, this also may cause a side effect known as network memorization which can occur again if the input training set is too small. This occurs because the network is so large that it may start to memorize sets of input chunks or potentially whole images or sets of text.

The cutting-edge DL models that you may have heard about like the GPT-3, a natural language processor from OpenAI, suffer in part from memorization. This is even after feeding billions of documents representing multiple forms of text into such models. Even with such diverse and massive training sets models like GPT-3 have been shown to replay whole paragraphs of remembered text. Which may be an effective feature for a database that doesn’t fit well into a DL model.

There have been workarounds developed for the memorization problem called dropout, a process by which a certain percentage of the nodes within network layers may be deactivated through each training pass. The result of turning off/on nodes within each pass creates a more general network. Yet at a cost of still requiring the network to now be 100 $200 \%$ larger.

## 计算机代写|深度学习代写deep learning代考|What is Automated Machine Learning, AutoML?

AutoML or automated machine learning is a tool or set of tools used to automate and enhance the building of $\mathrm{AI} / \mathrm{ML}$. It is not a specific technology but a collection of methods and strategies in which evolutionary algorithms or evolutionary optimization methods would be considered a subset. It is a tool that can be used throughout the $\mathrm{AI} / \mathrm{ML}$ workflow as depicted in Figure 1.3.

Figure $1.1$ depicts the typical AI/ML workflow for building a good model used later for confident inference of new data. This workflow is often undertaken manually by various oractitioners of AI/ML but there have been various attempts to automate all steps. Below is a summary of each of these steps in more detail and how they may be automated with AML:

expensive. In general, preparing data Automating this task can dramatically increase the performance of data workflows critical to fine-tuning complex models. AutoML online services often assume that the user has already prepared and cleaned data as required by most ML models. With evolutionary methods, there are several ways to automate the preparation of data and while this task is not specific to EDL, we will cover it in later chapters.

• Feature Engineering – is the process of extracting relevant features in data using prior domain knowledge. With experts picking and choosing relevant features based on their intuition and experience. Since domain experts are expensive and opinionated, automating this task reduces costs and improves standardization. Depending on the AutoML tool feature engineering may be included in the process.
• Model Selection – as AI/ML has advanced there are now hundreds of various model types that could be used to solve similar problems. Often data scientists will spend days or weeks just selecting a group of models to further evaluate. Automating this process speeds up model development and helps the data scientist affirm they are using the right model for the job. A good AutoML tool may choose from dozens or hundreds of models including DL variations or model ensembles.
• Model Architecture – depending on the area of $\mathrm{AI} / \mathrm{ML}$ and deep learning, defining the right model architecture is often critical. Getting this right in an automated way alleviates countless hours of tuning architecture and rerunning models. Depending on the implementation some AutoML systems may vary model architecture, but this is typically limited to well-known variations.
• Hyperparameter Optimization – the process of fine-tuning a model’s hyperparameters can be time-consuming and error-prone. To overcome this, many practitioners rely on intuition and previous experience. While this has been successful in the past, increasing model complexity now makes this task untenable. By automating HP tuning we not only alleviate work from the builders but also uncover potential flaws in the model selection or architecture.
• Validation Selection – there are many options for evaluating the performance of a model. From deciding on how much data to use for training and testing to visualizing the output performance of a model. Automating the validation of a model provides a robust means to recharacterize model performance when data changes and makes a model more explainable long term. For online AutoML services, this is a key strength that provides a compelling reason to employ such tools.

## 计算机代写|深度学习代写deep learning代考|What is Automated Machine Learning, AutoML?

AutoML 或自动化机器学习是一种工具或一组工具，用于自动化和增强构建一种我/米大号. 它不是一种特定的技术，而是一种方法和策略的集合，其中进化算法或进化优化方法将被视为一个子集。它是一个可以在整个过程中使用的工具一种我/米大号工作流程如图 1.3 所示。

• 特征工程——是使用先验领域知识从数据中提取相关特征的过程。专家根据他们的直觉和经验挑选和选择相关特征。由于领域专家的费用昂贵且固执己见，因此自动化此任务可降低成本并提高标准化程度。根据 AutoML 工具的不同，特征工程可能包含在该过程中。
• 模型选择——随着 AI/ML 的进步，现在有数百种不同的模型类型可用于解决类似的问题。数据科学家通常会花费数天或数周的时间来选择一组模型进行进一步评估。自动化此过程可加快模型开发并帮助数据科学家确认他们正在使用正确的模型来完成工作。一个好的 AutoML 工具可能会从数十个或数百个模型中进行选择，包括 DL 变体或模型集成。
• 模型架构——取决于区域一种我/米大号和深度学习，定义正确的模型架构通常是至关重要的。以自动化方式正确完成此操作可以减少无数小时的架构调整和重新运行模型。根据实现的不同，一些 AutoML 系统可能会改变模型架构，但这通常仅限于众所周知的变体。
• 超参数优化——微调模型超参数的过程可能既耗时又容易出错。为了克服这个问题，许多从业者依靠直觉和以往的经验。虽然这在过去是成功的，但现在增加的模型复杂性使这项任务变得难以维持。通过自动化 HP 调整，我们不仅可以减轻构建者的工作量，还可以发现模型选择或架构中的潜在缺陷。
• 验证选择——有许多选项可用于评估模型的性能。从决定用于训练和测试的数据量到可视化模型的输出性能。自动验证模型提供了一种强大的方法，可以在数据发生变化时重新表征模型性能，并使模型在长期内更易于解释。对于在线 AutoML 服务，这是一个关键优势，它提供了使用此类工具的令人信服的理由。

## 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。