## 计算机代写|深度学习代写deep learning代考|KIT315

2022年12月27日

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• 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
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## 计算机代写|深度学习代写deep learning代考|Option Selection and Hyperparameter Tuning

One of the most difficult things practitioners of deep learning face is figuring out which options and knobs to dial in to improve their models. Most texts dedicated to teaching DL often address the many options and hyperparameters but rarely detail the effects of changes. This is compounded by an $\mathrm{AI} / \mathrm{ML}$ community showcasing state of the art models that often omit the vast amount of work needed to attain them.

For most practitioners, learning how to use the many options and tuning hyperparameters is learned through hours of experience building models. Without such tuning many such models, as was demonstrated in the last section, could be seriously flawed. Which not only becomes a problem for newcomers but the field of DL itself.

We will start by looking at a base deep learning model that uses PyTorch to approximate a function. If you are new to PyTorch consult Appendix B for a short introduction to working with this excellent framework. Later examples in this book will use Keras and/or PyTorch to demonstrate how easily these techniques can be swapped between frameworks.

In this section we will look at some techniques and strategies to select options and tune hyperparameters for DL models. Some of these have been glimmered from years of experience but realize such strategies will constantly need to evolve. DL is constantly growing, and new model options are continually being enlisted.

A few key differences have been added to demonstrate working with hyperparameters and other options for the exercise below:

1. Open the notebook EDL_5_1_Hyperparameter_Tuning.ipynb in your browser. Consult Appendix A if you need assistance and Appendix B if you want to review the initial setup taken from EDL_B_PyTorchBasics.ipynb.
2. Start by running the whole notebook from the menu with Run $\rightarrow$ Run all. Confirm that the output is like figure $5.1$ for the initial function and predicted solution.

## 计算机代写|深度学习代写deep learning代考|Selecting Model Options

Aside from hyperparameters the biggest source of tuning the model will come in the various options you internally decide to use. DL models provide for numerous options sometimes dictated by the problem or network architecture, but often subtle variations may radically alter the way a model fit.

Model options range from activation and optimizer functions to the addition of the number and size of layers. As we mentioned in the last section layer depth is often dictated by the number of features a model needs to extract and learn. The type of layer, be it convolutional or recurrent networks, is often determined by the type of features needed to be learned. Where we use CNN layers to learn clusters of features and RNN to determine how features are aligned or in what order.

Therefore, most DL models network size and layer types will be driven by the variance of data and the type of features needed to be learned. For image classification problems CNN layers are used to extract visual features like an eye or mouth. While RNN layers are used for processing language or time data where the need is to understand how one feature relates to another in sequences.

That means that in most cases the options a DL practitioner will need to concern themselves with are the base functions of activation, optimization, and loss. Activation functions will typically be dictated by the type of problem and form of data. We typically avoid altering activation functions until the final steps of tuning.

Most often the choice of optimizer and loss function will dictate how well a model trains, if at all. Take for example figure $5.5$ which shows the results of selecting 3 different optimizers to train our last exercise using a middle_layer hyperparameter of 25 . Notice in the figure that stochastic gradient descent (SGD) and Adagrad perform quite poorly in comparison to Adam and RMSprop.

## 计算机代写|深度学习代写deep learning代考|Option Selection and Hyperparameter Tuning

1. 在浏览器中打开笔记本 EDL_5_1_Hyperparameter_Tuning.ipynb。如果您需要帮助，请参阅附录 A；如果您想查看从 EDL_B_PyTorchBasics.ipynb 获取的初始设置，请参阅附录 B。
2. 首先从菜单中使用 Run 运行整个笔记本→全部运行。确认输出如图5.1用于初始函数和预测解。

## 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。