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assignmentutor-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|Classical Approaches for Image Classification

Although the SVM and its kernel extension are beautiful convex optimization frameworks devoid of local minimizers, there are fundamental challenges in using these methods for image classification. In particular, the ambient space $\mathcal{X}$ should not be significantly large in the SVM due to the computationally extensive optimization procedure. Accordingly, one of the essential steps of using the SVM framework is feature engineering, which pre-processes the input images to obtain significantly smaller dimensional vector $\boldsymbol{x} \in \mathcal{X}$ that can capture all essential information of the input images. For example, a classical pipeline for the image classification task can be summarized as follows (see Fig. 2.7):

• Process the data set to extract hand-crafted features based on some knowledge of imaging physics, geometry, and other analytic tools,
• or extract features by feeding the data into a standard set of feature extractors such as SIFT (the Scale-Invariant Feature Transform) [12], or SURF (the Speeded-Up Robust Features) [13], etc.
• Choose the kernels based on your domain expertise.
• Put the training data composed of hand-crated features and labels into a kernel SVM to learn a classifier.

Here, the main technical innovations usually comes from the feature extraction, often based on the serendipitous discoveries of lucky graduate students. Moreover, kernel selection also requires domain expertise that was previously the subject of extensive research. We will see later that one of the main innovations in the modern deep learning approach is that this hand-crafted feature engineering and kernel design are no longer required as they are automatically learned from the training data. This simplicity can be one of the main reasons for the success of deep learning, which led to the deluge of new deep tech companies.

So far we have mainly discussed the binary classification problems. Note that more general forms of the classifiers beyond the binary classifier are of importance in practice: for example. ImageNet has more than $20.000$ categories. The extension of the linear classifier for such a setup is important, but will be discussed later.

计算机代写|深度学习代写deep learning代考|Linear, Logistic, and Kernel Regression

In machine learning, regression analysis refers to a process for estimating the relationships between dependent variables and independent variables. This method is mainly used to predict and find the cause-and-effect relationship between variables. For example, in a linear regression, a researcher tries to find the line that best fits the data according to a certain mathematical criterion (see Fig. 3.1a). Another important regression problem is the logistic regression. For example, in Fig. 3.1b, the dependent variables are binary properties such as yes or no for a given question, and the goal is to fit the binary data using continuously varying independent variables. It is easy to understand that this problem is closely related to the binary classification problem. For the case of Fig.3.1c, the technical issue is a bit different from the other two. Here, the distribution cannot be regressed out by a linear line. Moreover, the dependent variable is not binary, but has continuous values. In fact, a better regression approach is to fit the data with a smoothly varying curve. In fact, this is directly related to a nonlinear regression problem.

Although regression analysis is a classical approach that can be dated back to the least squares method by Legendre in 1805 and by Gauss in 1809 , regression analysis is still a key idea of the deep learning approaches, as will be discussed later. Therefore, we will visit the classical regression approach to discuss three specific forms of regression analysis: linear regression, logistic regression, and kernel regression. Later on, this overview will prove useful in understanding modern regression approaches using deep neural networks.

深度学习代写

计算机代写|深度学习代写deep learning代考|Classical Approaches for Image Classification

• 处理数据集以提取基于成像物理、几何和其他分析工具的一些知识的手工制作的特征，
• 或通过将数据输入到一组标准的特征提取器中来提取特征，例如 SIFT（尺度不变特征变换）[12] 或 SURF（加速鲁棒特征）[13] 等。
• 根据您的领域专业知识选择内核。
• 将由手工制作的特征和标签组成的训练数据放入内核 SVM 以学习分类器。

有限元方法代写

assignmentutor™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。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师