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# 计算机代写|基础编程代写Fundamental of Programming代考|KIT101 Deep Learning

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## 计算机代写|基础编程代写Fundamental of Programming代考|Deep Learning

Neural networks are one of the few machine learning algorithms that directly support higher dimensional data, such as vector data or even higher-dimensional data representing images and videos [8]. Additionally, deep neural networks are often advertised as machine learning technique that eliminates any manual feature engineering, because feature engineering is already performed as part of the network and it’s many layers. As a result, deep neural networks offers some powerful features that are not established well in GP yet. However, neural network lack the simplicity and interpretability of symbolic models, therefore, the knowledge on how features were extracted from the higher dimensional data, and how those effect a model’s output is very difficult to assess.

There is an also interesting technical intersection between GP and neural networks regarding the representation of models. Most neural networks libraries, such as TensorFlow ${ }^3$ or PyTorch, ${ }^4$ use directed acyclic graphs of tensor operations for representing a neural network, similar to the graph representation of Cartesian GP [16]. Such tensor-graphs could also be used to represent symbolic models that contain scalar and vector data. The additional benefit of representing models as directed graphs is it’s ability to automatically calculate the gradient of such a graph via automatic differentiation [9], which is essential for training neural networks. Being able to calculate the gradient of a symbolic model also allows optimizing the numerical constants of a given symbolic model via least squares, which is more efficient than tuning the coefficients via GP [13].

## 计算机代写|基础编程代写Fundamental of Programming代考|Grammar-Based Vectorial Genetic Programming

In this section, we present our method that extends classical tree-based genetic programming for symbolic regression, to also support vector variables along scalar variables. Our method combines the vectorial GP approach described in Sect. 2.2.1 and a grammar-based approach for symbolic regression described in Sect. 2.2.2. Fortunately, both vectorial GP and grammar-based GP can be implemented independently since they affect independent aspects of GP.

On the one hand, extending GP to be able to handle vector variables only affects the interpretation module of GP, which is responsible for evaluating a tree-model, by applying operations defined in the sub-trees onto its argument and propagating the results bottom-up until it reaches the root of the tree. On the other hand, the grammar limits the search space by restricting random creation, crossover and mutator to only create models that adhere to the specified grammar. Therefore, while the interpretation module should be as powerful as possible and allow evaluation of all valid models, the grammar is responsible of limiting the search space to models that are sensible and the users are interested in.

## 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。