### 计算机代写|深度学习代写deep learning代考|COMP5329

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
• 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

## 计算机代写|深度学习代写deep learning代考|Conway’s Game of Life on Google Collaboratory

In this next section we are going to explore the Game of Life by John Horton Conway. This simple cellular automation developed in 1970 is attributed to the birth of the computer simulation. While the rules of the simulation are simple the patterns and manifestations it can produce are an incredible testament to its eloquence.

This next exercise will also help us introduce Google Collaboratory or Colab as it is widely known and the term, we will refer to it by. Colab is an excellent platform for performing all forms of machine learning from evolutionary computation to deep learning. It is based on Jupyter notebooks so should be familiar to most Python developers with a notebook background. Furthermore, it is free and provides both CPU and GPU resources we will heavily use later.

1. Begin the exercise by loading up the exercise
EDL_2_1_Conways_Game_of_Life.ipynb in your browser. Please refer to appendix A to get details on how to load the code from the GitHub repository to Colab.
2. After you open the notebook in Colab you will see several text and code cells. We won’t worry about any of the code in this exercise, just the steps on how to use Colab to execute the notebook and explore the results.
3. Next, select the first code cell in the notebook and click the Run Cell button in the top left or type Ctrl+Enter or Cmd+Enter to run the cell. This will run the code and setup the show_video function to be use later. We employ this function to demonstrate a real-time visual output of the simulation.

## 计算机代写|深度学习代写deep learning代考|Life Simulation as Optimization

In this next scenario, we are going to use our previous simple example and elevate it to perform optimization of an attribute defined on the cells. There are many reasons we may develop simulations for all forms of discovery of behavior, optimization, or enlightenment. For most applications of evolutionary algorithms, our end goal will be to optimize a process, parameters, or structure.

For this next notebook, we extend the attributes in each cell from health to include a new parameter called strength. Our goal will be to optimize the cell strength of our entire population. Strength will be representative of any trait in an organism that makes it successful in its environment. That means in our simple example our goal will be to maximize strength across the entire population.

1. Open the notebook example EDL_2_3_Simulating_Life_part2.ipynb in your browser. Check appendix $\mathrm{A}$ if you require assistance.
2. We are using a useful real-time plotting library called LivelossPlot for several examples in this book. This library is intended for plotting training losses for machine and deep learning problems. So, the default graphs present terminology we would use in a DL problem but nonetheless, it will work perfectly fine for needs. The code below demonstrates installing the package and importing the PlotLosses class.
3. The bulk of the code in this example is shared from the previous and as such we will just look at the differences. Starting with the first cell we can see a few changes in the functions that define the life simulation shown below. The big change here is that we now use the new strength parameter to derive the cell’s health.
4. Likewise, the reproduction and death functions have been modified to not pick random cells to reproduce or die. Instead, the new functions determine if a cell reproduces or dies based on the health attribute. Notice the addition of 2 new parameters, reproduction bounds and death bounds. These new parameters control at what health level a cell can reproduce or when it should die.

## 计算机代写|深度学习代写deep learning代考|Conway’s Game of Life on Google Collaboratory

1. 通过在浏览器中加载练习 EDL_2_1_Conways_Game_of_Life.ipynb 来开始练习。请参阅附录 A 以获取有关如何将代码从 GitHub 存储库加载到 Colab 的详细信息。
2. 在 Colab 中打开笔记本后，您将看到几个文本和代码单元格。我们不会担心本练习中的任何代码，只需关注有关如何使用 Colab 执行笔记本并探索结果的步骤。
3. 接下来，选择笔记本中的第一个代码单元格，然后单击左上角的“运行单元格”按钮或键入 Ctrl+Enter 或 Cmd+Enter 来运行该单元格。这将运行代码并设置 show_video 函数以供稍后使用。我们使用此功能来演示模拟的实时视觉输出。

## 计算机代写|深度学习代写deep learning代考|Life Simulation as Optimization

1. 在浏览器中打开笔记本示例 EDL_2_3_Simulating_Life_part2.ipynb。检查附录一种如果您需要帮助。
2. 对于本书中的几个示例，我们使用了一个名为 LivelossPlot 的有用实时绘图库。该库旨在绘制机器和深度学习问题的训练损失。因此，默认图表提供了我们将在 DL 问题中使用的术语，但尽管如此，它仍然可以很好地满足需要。下面的代码演示了安装包和导入 PlotLosses 类。
3. 此示例中的大部分代码与之前的代码相同，因此我们将只查看不同之处。从第一个单元格开始，我们可以看到定义如下所示的生命模拟的函数发生了一些变化。这里最大的变化是我们现在使用新的强度参数来推导细胞的健康状况。
4. 同样，繁殖和死亡功能已被修改为不选择随机细胞进行繁殖或死亡。相反，新函数根据健康属性确定细胞是繁殖还是死亡。注意添加了 2 个新参数，即繁殖界限和死亡界限。这些新参数控制细胞可以在什么健康水平下繁殖或何时死亡。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。