## 计算机代写|深度学习代写deep learning代考|STAT3007

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|Subdifferentials

The directional derivative of $f$ at $\boldsymbol{x} \in \operatorname{dom} f$ in the direction of $\boldsymbol{y} \in \mathcal{H}$ is defined by
$$f^{\prime}(x ; y)=\lim _{\alpha \downarrow 0} \frac{f(x+\alpha y)-f(x)}{\alpha}$$ if the limit exists. If the limit exists for all $y \in \mathcal{H}$, then one says that $f$ is Gãteaux differentiable at $\boldsymbol{x}$. Suppose $f^{\prime}(\boldsymbol{x} ; \cdot)$ is linear and continuous on $\mathcal{H}$. Then, there exist a unique gradient vector $\nabla f(\boldsymbol{x}) \in \mathcal{H}$ such that
$$f^{\prime}(\boldsymbol{x} ; \boldsymbol{y})=\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{y} \in \mathcal{H}$$
If a function is differentiable, the convexity of a function can easily be checked using the first- and second-order differentiability, as stated in the following:

Proposition $1.1$ Let $f: \mathcal{H} \mapsto(-\infty, \infty]$ be proper. Suppose that $\operatorname{dom} f$ is open and convex, and $f$ is Gâteux differentiable on $\operatorname{dom} f$. Then, the followings are equivalent:

1. $f$ is convex.
2. (First-order): $f(\boldsymbol{y}) \geq f(\boldsymbol{x})+\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$.
3. (Monotonicity of gradient): $\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{y})-\nabla f(\boldsymbol{x})\rangle \geq 0, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$.
If the convergence in (1.48) is uniform with respect to $\boldsymbol{y}$ on bounded sets, i.e.
$$\lim _{\boldsymbol{0} \neq \boldsymbol{y} \rightarrow \mathbf{0}} \frac{f(\boldsymbol{x}+\boldsymbol{y})-f(\boldsymbol{x})-\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle}{|\boldsymbol{y}|}=0$$

## 计算机代写|深度学习代写deep learning代考|Linear and Kernel Classifiers

Classification is one of the most basic tasks in machine learning. In computer vision, an image classifier is designed to classify input images in corresponding categories. Although this task appears trivial to humans, there are considerable challenges with regard to automated classification by computer algorithms.

For example, let us think about recognizing “dog” images. One of the first technical issues here is that a dog image is usually taken in the form of a digital format such as JPEG, PNG, etc. Aside from the compression scheme used in the digital format, the image is basically just a collection of numbers on a twodimensional grid, which takes integer values from 0 to 255 . Therefore, a computer algorithm should read the numbers to decide whether such a collection of numbers corresponds to a high-level concept of “dog”. However, if the viewpoint is changed, the composition of the numbers in the array is totally changed, which poses additional challenges to the computer program. To make matters worse, in a natural setting a dog is rarely found on a white background; rather, the dog plays on the lawn or takes a nap in the living room, hides underneath furniture or chews with her eyes closed, which makes the distribution of the numbers very different depending on the situation. Additional technical challenges in computer-based recognition of a dog come from all kinds of sources such as different illumination conditions, different poses, occlusion, intra-class variation, etc., as shown in Fig. 2.1. Therefore, designing a classifier that is robust to such variations was one of the important topics in computer vision literature for several decades.

In fact, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [7] was initiated to evaluate various computer algorithms for image classification at large scale. ImageNet is a large visual database designed for use in visual object recognition software research [8]. Over 14 million images have been hand-annotated in the project to indicate which objects are depicted, and at least one million of the images also have bounding boxes. In particular, ImageNet contains more than 20,000 categories made up of several hundred images. Since 2010, the ImageNet project has organized an annual software competition, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), in which software programs compete for the correct classification and recognition of objects and scenes. The main motivation is to allow researchers to compare progress in classification across a wider variety of objects. Since the introduction of AlexNet in 2012 [9], which was the first deep learning approach to win the ImageNet Challenge, the state-of-the art image classification methods are all deep learning approaches, and now their performance even surpasses human observers.

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

$$f^{\prime}(x ; y)=\lim _{\alpha \downarrow 0} \frac{f(x+\alpha y)-f(x)}{\alpha}$$

$$f^{\prime}(\boldsymbol{x} ; \boldsymbol{y})=\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{y} \in \mathcal{H}$$

1. $f$ 是凸的。
2. (第一个订单) : $f(\boldsymbol{y}) \geq f(\boldsymbol{x})+\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{x})\rangle, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$.
3. (梯度的单调性) : $\langle\boldsymbol{y}-\boldsymbol{x}, \nabla f(\boldsymbol{y})-\nabla f(\boldsymbol{x})\rangle \geq 0, \quad \forall \boldsymbol{x}, \boldsymbol{y} \in \mathcal{H}$. 如果 (1.48) 中的收敛是一致的 $\boldsymbol{y}$ 在有界集上，即
$$\lim _{\boldsymbol{0} \neq \boldsymbol{y} \rightarrow 0} \frac{f(\boldsymbol{x}+\boldsymbol{y})-f(\boldsymbol{x})-\langle\boldsymbol{y}, \nabla f(\boldsymbol{x})\rangle}{|\boldsymbol{y}|}=0$$

## 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。

## 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
• (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|Some Definitions

Let $\mathcal{X}, \mathcal{Y}$ and $Z$ be non-empty sets. The identity operator on $\mathcal{H}$ is denoted by $I$, i.e. $I x=x, \forall x \in \mathcal{H}$. Let $\mathcal{D} \subset \mathcal{H}$ be a non-emply sel. The set of the fixed points of an operator $\mathcal{T}: D \mapsto D$ is denoted by
$$\operatorname{Fix} \mathcal{T}={x \in \mathcal{D} \mid \mathcal{T} x=x}$$
Let $\mathcal{X}$ and $\mathcal{Y}$ be real normed vector space. As a special case of an operator, we define a set of linear operators:
$$\mathcal{B}(\mathcal{X}, \mathcal{Y})={\mathcal{T}: \mathcal{Y} \mapsto \mathcal{Y} \mid \mathcal{T} \text { is linear and continuous }}$$
and we write $\mathcal{B}(\mathcal{X})=\mathcal{B}(\mathcal{X}, \mathcal{X})$. Let $f: \mathcal{X} \mapsto[-\infty, \infty]$ be a function. The domain of $f$ is
$$\operatorname{dom} f={\boldsymbol{x} \in \mathcal{X} \mid f(\boldsymbol{x})<\infty}$$
the graph of $f$ is
$$\operatorname{gra} f={(\boldsymbol{x}, y) \in \mathcal{X} \times \mathbb{R} \mid f(\boldsymbol{x})=y},$$
and the epigraph of $f$ is
$$\text { eنi } f={(x, y) . x \in X, y \in \mathbb{R}, y \geq f(x)} \text {. }$$

## 计算机代写|深度学习代写deep learning代考|Convex Sets, Convex Functions

A function $f(\boldsymbol{x})$ is a convex function if $\operatorname{dom} f$ is a convex set and
$$f\left(\theta \boldsymbol{x}{1}+(1-\theta) \boldsymbol{x}{2}\right) \leq \theta f\left(\boldsymbol{x}{1}\right)+(1-\theta) f\left(\boldsymbol{x}{1}\right)$$
for all $x_{1}, x_{2} \in \operatorname{dom} f, 0 \leq \theta \leq 1$. A convex set is a set that contains every line segment between any two points in the set (see Fig. 1.3). Specifically, a set $C$ is convex if $\boldsymbol{x}{1}, \boldsymbol{x}{2} \in \mathcal{C}^{\prime}$, then $\theta \boldsymbol{x}{1}+(1-\theta) \boldsymbol{x}{2} \in \mathcal{C}$ for all $0 \leq \theta \leq 1$. The relation between a convex function and a convex set can also be stated using its epigraph. Specifically, a function $f(x)$ is convex if and only if its epigraph epi $f$ is a convex set.

Convexity is preserved under various operations. For example, if $\left{f_{i}\right}_{i \in I}$ is a family of convex functions, then, $\sup {i \in I} f{i}$ is convex. In addition, a set of convex functions is closed under addition and multiplication by strictly positive real numbers. Moreover, the limit point of a convergent sequence of convex functions is also convex. Important examples of convex functions are summarized in Table $1.1$.

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

$\operatorname{Fix} \mathcal{T}=x \in \mathcal{D} \mid \mathcal{T} x=x$

$\mathcal{B}(\mathcal{X}, \mathcal{Y})=\mathcal{T}: \mathcal{Y} \mapsto \mathcal{Y} \mid \mathcal{T}$ is linear and continuous

$$\operatorname{dom} f=\boldsymbol{x} \in \mathcal{X} \mid f(\boldsymbol{x})<\infty$$

$$\operatorname{gra} f=(\boldsymbol{x}, y) \in \mathcal{X} \times \mathbb{R} \mid f(\boldsymbol{x})=y,$$

$$\text { eui } f=(x, y) . x \in X, y \in \mathbb{R}, y \geq f(x)$$

## 计算机代写|深度学习代写deep learning代考|Convex Sets, Convex Functions

$$f(\theta \boldsymbol{x} 1+(1-\theta) \boldsymbol{x} 2) \leq \theta f(\boldsymbol{x} 1)+(1-\theta) f(\boldsymbol{x} 1)$$

## 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。

## 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|Metric Space

A metric space $(\mathcal{X}, d)$ is a set $\chi$ together with a metric $d$ on the set. Here, a metric is a function that defines a concept of distance between any two members of the set, which is formally defined as follows.

Definition 1.1 (Metric) A metric on a set $X$ is a function called the distance $d$ : $\mathcal{X} \times \mathcal{X} \mapsto \mathbb{R}{+}$, where $\mathbb{R}{+}$is the set of non-negative real numbers. For all $x, y, z \in \mathcal{X}$, this function is required to satisfy the following conditions:

1. $d(x, y) \geq 0$ (non-negativity).
2. $d(x, y)=0$ if and only if $x=y$.
3. $d(x, y)=d(y, x)$ (symmetry).
4. $d(x, z) \leq d(x, y)+d(y, z)$ (triangle inequality).
A metric on a space induces topological properties like open and closed sets, which lead to the study of more abstract topological spaces. Specifically, about any point $x$ in a metric space $\mathcal{X}$, we define the open ball of radius $r>0$ about $x$ as the set
$$B_{r}(x)={y \in \mathcal{X}: d(x, y)0 such that B_{r}(x) is contained in U. The complement of an open set is called closed. ## 计算机代写|深度学习代写deep learning代考|Banach and Hilbert Space An inner product space is defined as a vector space that is equipped with an inner product. A normed space is a vector space on which a norm is defined. An inner product space is always a normed space since we can define a norm as |f|= \sqrt{\langle\boldsymbol{f}, \boldsymbol{f}\rangle}, which is often called the induced norm. Among the various forms of the normed space, one of the most useful normed spaces is the Banach space. Definition 1.7 The Banach space is a complete normed space. Here, the “completeness” is especially important from the optimization perspective, since most optimization algorithms are implemented in an iterative manner so that the final solution of the iterative method should belong to the underlying space \mathcal{H}. Recall that the convergence property is a property of a metric space. Therefore, the Banach space can be regarded as a vector space equipped with desirable properties of a metric space. Similarly, we can define the Hilbert space. Definition 1.8 The Hilbert space is a complete inner product space. We can easily see that the Hilbert space is also a Banach space thanks to the induced norm. The inclusion relationship between vector spaces, normed spaces, inner product spaces, Banach spaces and Hilbert spaces is illustrated in Fig. 1.1. As shown in Fig. 1.1, the Hilbert space has many nice mathematical structures such as inner product, norm, completeness, etc., so it is widely used in the machine learning literature. The following are well-known examples of Hilbert spaces: • l^{2}(\mathbb{Z}) : a function space composed of square summable discrete-time signals, i.e.$$
l^{2}(\mathbb{Z})=\left{x=\left.\left{x_{l}\right}_{l=-\infty}^{\infty}\left|\sum_{l=-\infty}^{\infty}\right| x_{l}\right|^{2}<\infty\right} .
$$## 深度学习代写 ## 计算机代写|深度学习代写deep learning代考|Metric Space 度量空间 (\mathcal{X}, d) 是一个集合 \chi 连同一个指标 d 在片场。这里，度量是定义集合中任意两个成员之间距离概念的函 数，其正式定义如下。 定义 1.1 (度量) 集合上的度量 X 是一个叫做距离的函数 d: \mathcal{X} \times \mathcal{X} \mapsto \mathbb{R}+ ，在哪里 \mathbb{R}+ 是一组非负实数。对所 有人 x, y, z \in \mathcal{X} ，该函数需要满足以下条件: 1. d(x, y) \geq 0 (非消极性) 。 2. d(x, y)=0 当且仅当 x=y. 3. d(x, y)=d(y, x) (对称)。 4. d(x, z) \leq d(x, y)+d(y, z) (三角不等式)。 空间上的度量会引发诸如开集和闭集之类的拓扑性质，从而导致对更抽象的拓扑空间的研究。具体来说，关 于任何一点 x 在度量空间 \mathcal{X} ，我们定义半径的开球 r>0 关于 x 作为集合 \ \$$
$\mathrm{B}{-}{\mathrm{r}}(\mathrm{x})=\left{\mathrm{y} \backslash\right.$ in Imathcal ${\mathrm{X}}: \mathrm{d}(\mathrm{x}, \mathrm{y}) 0$ suchthat $\mathrm{B}{-}{\mathrm{r}}(\mathrm{x})$ iscontainedin 美元。开集的补集称为闭集。

## 计算机代写|深度学习代写deep learning代考|Banach and Hilbert Space

• $l^{2}(\mathbb{Z})$ : 由平方和离散时间信号组成的函数空间，即

## 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。

## 机器学习代写|深度学习project代写deep learning代考|DEEP LEARNING TECHNIQUES FOR THE PREDICTION OF EPILEPSY

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

## 机器学习代写|深度学习project代写deep learning代考|ARTIFICIAL INTELLIGENCE

Deep learning helps simulation techniques with various computing layers to gain several stages of abstraction for data representations. These techniques have vastly enhanced the position in voice detection, visual target recognition, particle identification as well as a variety of other fields including drug discovery as well as genomics. Deep learning uses the backpropagation method to show how a computer can adjust the input variables that are employed to measure the value in every layer from the description in the subsequent layer revealing detailed structure in huge volumes of data [1]. Deep learning is perhaps the highest accuracy, supervised as well as time and cost-effective machine learning method. Deep learning is not a limited learning methodology rather it encompasses a wide range of methods that can be employed in a wide range of complex situations [2].

Let’s start with a definition of intelligence. Intelligence is defined as the ability to learn and solve issues. Its primary goal is to create computers so clever that they can act intelligently in the same way that humans do. If a computer learns new information, it can intelligently solve real-world problems based on previous experiences. As we all know, intelligence is the capacity to learn and solve issues, and intelligence is attained via knowledge, which is attained in part via information, which is attained via prior experiences and experiences obtained through training. Finally, by combining all of the elements, we can conclude that artificial intelligence can obtain knowledge and apply it to execute tasks intelligently based on their previous experiences. Reasoning, learning, problem solving, and perception are all aspects of intelligence. Artificial intelligence systems are required to minimize human workload. Natural Language Processing (NLP), Speech Recognition, Healthcare, Vision Systems, and Automotive are just a few of the applications. An agent and its surroundings make up an Artificial Intelligence system. The environment is perceived by sensors, and the environment is reacted to by effectors. An intelligent agent sets objectives and is extremely interested in achieving them. Artificial Intelligence has developed some tools to help handle tough and complicated issues, including neural networks, languages, search and optimization, and uncertain reasoning, among others.

## 机器学习代写|深度学习project代写deep learning代考|MACHINE LEARNING

Machine learning is the subset of artificial intelligence (AI). Machine learning, as the name implies, is the capacity of a machine to learn. Machine learning is a branch of computer science that allows computers to learn and solve problems without being explicitly programmed. Here, we create a machine that performs duties similar to those performed by humans to decrease human labor. It is a branch of research that enables computers to learn new things using information fed to them and to produce more efficient output using that information. It is employed in a variety of professions and has gained notoriety in a variety of sectors. It is a fantastic technology that allows machines or computers to learn and solve complicated problems.

The term “deep learning” comes first from artificial neural networks [3]. A convolutional neural network is perhaps the most essential of the several deep learning networks since it considerably encourages the growth of image analysis. Generative adversarial network as a novel deep learning model offers up new boundaries for the study as well as application of deep learning which has lately received a lot of attention [4].

The biggest thrilling utilization of backpropagation since it was initially developed was for recurrent neural networks (RNNs) simulation. RNNs are also preferred for functions that need sequential inputs, like expression and vocabulary. It produces the output based on previous computation by using sequential information. Recurrent Neural Networks are similar to neural networks, however, they do not function in the same way. As humans, we do not think from the ground up. For example, if we’re watching a movie, we may guess what will happen next based on what we know about the prior one. A typical neural network, on the other hand, is unable to predict the next action in the film. Recurrent Neural Networks can address challenges like these. In a recurrent neural network, there is a loop in the network that keeps the data. It can take more than one input vector and produce more than one output vector. Recurrent neural networks use memory cells that are capable to capture information about long sequences. RNNs are extremely strong dynamic structures, although teaching them has proven difficult because backpropagated gradients expand or retreat at every time stage, causing them to burst or disappear over several time stages [7].

## 机器学习代写|深度学习project代写deep learning代考|CONVOLUTIONAL NEURAL NETWORK

The neural network image processing group was the first to create the convolutional neural network. Convolutional neural networks are a type of deep neural network used in deep learning to analyze pictures.

Convolutional neural networks are extensively employed in image identification, object identification, picture classifications, and face identification, among other applications. Convolution takes an input picture, works on it, and then uses the values to categorize it (either cat or dog, pen or pencil). ConvNets are developed to accommodate data in the form of several arrays, such as a color image made up of three $2 \mathrm{D}$ arrays representing pixel elevations in each of the color channels. As attribute extractors, a CNN uses two operations as convolution and pooling. As in a multi-layer perceptron, the output of this series of operations is bound to a completely connected layer. Convolutional neural networks are often used on text in Natural Language Processing. There are two types of pooling used: max-pooling and average-pooling. When we use CNN for text instead of images, we display the text with a 1-Dimensional string. CNN is mostly used in sentence classification in NLP tasks. Microsoft earlier released a range of optical character recognition software, as well as handwriting recognition software, specifically focuses on ConvNet [5]. ConvNets were still used to recognize objects in real photographs, such as faces as well as legs, and even to recognize faces in the early 1990 s [6].

Generative adversarial network (GAN) is a novel deep learning concept that includes a unique neural network model that trains generator as well as discriminator at the same time. The generator’s job is to know and understand the probability distribution of actual images and afterward add random noise to them to make false images, whereas the discriminator’s job is to determine whether a source variable is genuine or not [17]. The discriminator, as well as generator, has been tweaked to enhance their performance. The generative adversarial network training process is unique in that it has been using backpropagation to train as well as utilizes the confrontation of two neural networks as training metric, significantly reducing the training problem as well as improving the training effectiveness of the induced model. A generative adversarial network provides an opportunity to learn deep representation without having to label your training data extensively [18]. One of the most often used generative adversarial network applications is computer vision [19].

## 机器学习代写|深度学习project代写deep learning代考|MACHINE LEARNING

“深度学习”一词首先来自人工神经网络 [3]。卷积神经网络可能是几个深度学习网络中最重要的，因为它极大地促进了图像分析的发展。生成对抗网络作为一种新颖的深度学习模型，为深度学习的研究和应用提供了新的界限，最近受到了很多关注[4]。

## 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。

## 机器学习代写|深度学习project代写deep learning代考|DEEP LEARNING APPROACHES FOR THE PREDICTION oF BREAST CANCER

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

## 机器学习代写|深度学习project代写deep learning代考|DEEP LEARNING APPROACHES FOR THE PREDICTION oF BREAST CANCER

Breast cancer is a type of cancer that develops in the cells of the breast and is a fairly prevalent disease in women. Breast cancer, like lung cancer, is a life-threatening condition for women. A promising and significant tool is automated computer technologies, particularly machine learning, to facilitate and improve medical analysis and diagnosis. Due to the great dimensionality and complexity of this data, cancer diagnosis using gene expression data remains a challenge. There is still ambiguity in the clinical diagnosis of cancer and the identification of tumor-specific markers, despite decades of study. In this study, we discuss various feature extraction techniques on different kinds of datasets. We also discuss various deep learning approaches for cancer detection and the identification of genes important for breast cancer diagnosis.

Cancer is a deadly disease. According to a survey, thousands of people die due to cancer every year. It is the largest cause of death in the world. It is basically a disease in which there is abnormal growth of body cells which spreads to different parts of the body. If this disease is detected in the initial stage, then this disease can be cured. Cancer basically develops due to cell growth. It originates in one part of the body and has the ability to penetrate various organs. Possible symptoms of cancer are lumps, prolonged cough, abnormal bleeding, exercise weight gain etc. Tumors are formed by most malignancies, but not all tumors are malignant. Tumors do not spread to all parts of the body. It is an abnormal growth of body tissue-when abnormal cells are stored somewhere in the body, a group of tissues is formed, which we call a tumor. These cells continue to grow abnormally and add more and more cells to their group, irrespective of the body’s desire. These tumor cells are solid and fluid-filled. That process takes the form of growing cancer. This is known as metastasis. Cancer metastases are the leading cause of death-Carcinoma, melanoma, leukemia sarcoma and lymphoma are the most common cancers. Carcinomas arise in the skin, lungs, breasts, pancreas and other organs and glands. Lymphomas are lymphocyte malignancies. Leukemia [6] is a type of blood cancer. Melanomas are malignancies that develop in the cells that produce skin pigment. Breast cancer mainly occurs in women, but it is not that men cannot fall prey to it.

## 机器学习代写|深度学习project代写deep learning代考|RELATED WORK

A support vector machine (SVM) with a dot-product kernel was utilised. Sahiner et al. [2] devised a method for extracting speculation and circumscribing margin features. Both features were quite accurate in describing bulk margins using BI-RADS descriptors. Weatherall et al. [4] proposed a method with a score of $0.93$. The tumour size correlation coefficient between MRI and pathologic analysis was the best. When compared to histologic measurement, the correlation coefficients for physical exam and x-ray mammography (available for 17 patients) were $0.72$ and $0.63$, respectively. The MRI accuracy was unaffected by the extent of cancer residua. To see how well different imaging modalities might reliably describe the extent of a breast cancer whose location was already established. As a result, data on 20 post-chemotherapy breast cancer patients aged 32 to 66 years old was collected retrospectively. Yeung et al. [5] proposed to determine the estimations of residual tumour via each modality; the preoperative clinical and imaging records were evaluated. These results were compared to the pathologist’s report’s histologic measurements of the live tumour. Because of the enormous number of genes, the high quantity of noise in gene expression data, and the complexity of biological networks, it is necessary to thoroughly evaluate the raw data and utilise the relevant gene subsets. Other approaches, such as principal component analysis (PCA), have been proposed for reducing the dimensionality of expression profiles in order to help group important genes in the context of expression profiles. Bengio et al. [6] proposed Auto encoders are strong and adaptable because they extract both linear and nonlinear connections from input data. As opposed to decreasing the dimension in one step, the SDAE encoder reduces the dimensionality of the gene expression data stack by stack, resulting in less information loss. Golub et al. [8] present microarray or RNA-seq data are thoroughly explored as a classification and grouping of gene expression. Using gene expression profiles and supervised learning algorithms, numerous ways for classifying cancer cells and healthy cells have been developed. In the analysis of leukaemia cancer cells, a self-organizing map (SOM). The phases depicted in Figure 1 are followed by the majority of image processing algorithms. The screen film mammographic images must be scanned before they can be processed. One of the advantages of digital mammography is that the picture can be processed immediately. The first stage in image processing is picture pre-processing. To reduce noise and improve image quality, it must be conducted on digitised pictures. The majority of digital mammogram pictures are of high quality. If the picture is an MLO view, removing the backdrop region and the pectoral muscle from the breast area is also part of the pre-processing stage. The objective of the segmentation procedure is to discover areas of suspicious interest (ROIs), including abnormalities. In the feature extraction process, the features are computed from the attributes of the region of interest. A significant difficulty in algorithm design is the feature selection step, in which the best collection of features is chosen for preventing false positives and identifying lesion types. Choosing a smaller feature subset that delivers the highest value for a classifier performance function is referred to as feature selection. Finally, the classification stage reduces false positives and categorises lesions based on predetermined criteria.

## 机器学习代写|深度学习project代写deep learning代考|FEATURE EXTRACTION TECHNIQUES

In the field of computer vision or image analysis, features play an important role in identifying useful information. The component picture is subjected to several picture pre-processing techniques, such as binarization, normalisation, thresholding, scaling, and so on, before picture feature extraction.

Feature extraction is the process of decreasing the amount of resources needed to explain a huge amount of data. One of the primary issues in completing complicated data analysis is the number of variables involved. GF (General features) and DSF (domain-specific features) are two types of features. FE approaches like statistical approaches can be used to extract some aspects that are not clearly recognised.

First order statistics (FOS), Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), Neighbourhood Gray Tone Difference Matrix (NGTDM), and Statistical Feature Matrix are all examples of this (SFM). As illustrated in Table 2, signal processing FE approaches include law mask features, whereas transform domain approaches include Gabor wavelet, Fourier Power Spectrum (FPS) features, and discrete wavelet transform.

## 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。

## 机器学习代写|深度学习project代写deep learning代考|Metrics for Evaluation

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

## 机器学习代写|深度学习project代写deep learning代考|Following metrics are used for evaluation

Accuracy: It is defined as the percentage of correct predictions made by a classifier compared to the actual value of the label. It can also be defined as the average number of correct tests in all tests [13]. To calculate accuracy, we use the equation:
$$\text { Accuracy }=(T N+T P) /(T N+T P+F N+F P) .$$
Here, TP, TN, FP, and FN mean true positives, false negatives, false positives, and false negatives. True positive is a condition where if the class label of a record in a dataset is positive, the classifier predicts the same for that particular record. Similarly, a true negative is a condition where if the class label of a record in a dataset is negative, the classifier predicts the same for that particular record. False-positive is a condition where the class label of a record in a dataset is negative, but the classifier

predicts the class label as positive. Similarly, a false negative is a condition where the class label of a record in a dataset is positive. Still, the classifier predicts the class label as negative for that record [13].

Sensitivity: It is defined as the percentage of true positives identified by the classifier while testing. To calculate it, we use the equation:
Sensitivity $=(T P) /(T P+F N) .$
Specificity: It is defined as the percentage of true negatives which are rightly identified by the classifier during testing. To calculate it we use the equation:
$$\text { Specificity }=(T N) /(T N+F P) .$$

## 机器学习代写|深度学习project代写deep learning代考|Databases Available

Although several databases of fundus images are available publicly, the creation of quality retinal image databases is still in progress to train deep neural networks.

• DRIVE [14] (Digital Retinal Image for vessel Extraction) – This database contains 40 images collected from 400 samples of age 25 to 90 in the Netherland. Out of 40,7 shows mild DR, whereas others are normal. Each set, i.e., training and testing, includes 20 images of different patients. For every image, manual segmentation known as truths or gold standards of blood vessels is provided.
• STARE [15] (Structured Analysis of Retina) – This database contains 20 retinal fundus images taken using a fundus camera. Datasets are divided into two classes or categories, one contains normal images, and the other includes images with various lesions. CHASE [16] – contains 28 images of $1280 * 960$ pixels, taken from multi-ethnic children in England.
• Messidor and Messidor-2 [17] – These databases contain 1200 and 1748 images of the retina, respectively, taken from both eyes. Messidor- 2 is an extension of the Messidor database taken from 874 samples.
• EyePACS-1 [18] – This database contains macula-centred images of 9963 subjects taken from different cameras in May-October 2015 at EyePACS screening sites.
• APTOS [19] (Asia Pacific Tele-Ophthalmology Society) contains 3662 training images and 1928 testing images. Images are available with the ground truths classified based on severity of DR rating on a scale of 0 to 4 .
• Kaggle [20] – contains 88,702 images of the retina with different resolutions and are classified into 5 DR stages. Many images are of bad quality, and also some of the ground truths have incorrect labelling.
• IDRID [21] (Indian Diabetic Retinopathy Image Dataset) contains 516 retinal fundus images captured by a retinal specialist at an Eye Clinic located in Nanded, Maharashtra, India.
• DIARETDBI [22] contains 89 retinal fundus images of size $1500^{*} 1152$ pixels, including 5 normal images and all other 84 DR images.
• $\quad D D R[23]$ (Diagnosis of Diabetic Retinopathy) – contains 13,637 retinal fundus images showing five stages of DR. From the dataset, 757 images show DR lesions.
• Others like E-ophtha [24], HRF [25], ROC [26], and DR2 [27] etc.

## 机器学习代写|深度学习project代写deep learning代考|Process of Detection of DR Using Deep Learning

There are various numbers of supervised learning methods and unsupervised learning methods available for detecting Diabetic Retinopathy. Deep learning is one technique widely used in medical imaging applications like image classification, image segmentation, image retrieval, image detection, and registration of images. For the detection and classification of diabetic retinopathy, Deep Learning techniques or deep neural networks have been widely used. Deep neural networks produce outstanding results in the removal of default features and isolation. Unlike machine learning methods, the performance of deep learning methods increases with an increase in the number of training datasets because of an increase in learned features. There is a various number of deep neural networks like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), Autoencoders, RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), DSN (Deep Stacking Network), Self-Organizing Maps, etc. Still, CNN has been widely used in medical imaging and is highly effective [11]. Deep networks are much more powerful by using strategies such as the dropout function that helps the network produce relevant results even when few features are missing in the test dataset. In addition, ReLUs (Direct Line Units) function is used as a transfer function in CNNs that helps in effective training as they do not disappear too much like the sigmoid function and tangent function used by standard ANNs. The basic architecture of CNN is that it works in different layers like Convolutional layers, pooling layers, fully connected layers, Dropout, and Activation function at last. In the convolution layer, different types of filters are used to extract features from the image. The subsampling (or pooling) layer acts as feature selection and makes the network potent to changes in size and orientation of the image. Average pooling and max pooling are mostly used in the pooling layer. A fully connected layer is used to define the whole input image. Several pretrained CNN architectures are present at the moment on ImageNet, such as LeNet, AlexNet, VGG, ResNet, GoogleNet, and more.

## 机器学习代写|深度学习project代写deep learning代考|Following metrics are used for evaluation

准确性 =(吨ñ+吨磷)/(吨ñ+吨磷+Fñ+F磷).

特异性 =(吨ñ)/(吨ñ+F磷).

## 机器学习代写|深度学习project代写deep learning代考|Databases Available

• DRIVE [14]（用于血管提取的数字视网膜图像）——该数据库包含从荷兰 25 至 90 岁的 400 个样本中收集的 40 张图像。在 40,7 中显示轻度 DR，而其他则正常。每组，即训练和测试，包括20张不同患者的图像。对于每张图像，都提供了称为血管真相或黄金标准的手动分割。
• STARE [15]（视网膜结构分析）——该数据库包含 20 张使用眼底照相机拍摄的视网膜眼底图像。数据集分为两类或类别，一类包含正常图像，另一类包含具有各种病变的图像。CHASE [16] – 包含 28 张图片1280∗960像素，取自英格兰的多种族儿童。
• Messidor 和 Messidor-2 [17] – 这些数据库分别包含 1200 和 1748 张从双眼拍摄的视网膜图像。Messidor-2 是从 874 个样本中提取的 Messidor 数据库的扩展。
• EyePACS-1 [18] – 该数据库包含 2015 年 5 月至 2015 年 10 月在 EyePACS 筛查站点从不同相机拍摄的 9963 名受试者的黄斑中心图像。
• APTOS [19]（亚太远程眼科学会）包含 3662 张训练图像和 1928 张测试图像。图像提供了根据 DR 等级的严重程度分类的基本事实，范围为 0 到 4。
• Kaggle [20] – 包含 88,702 张不同分辨率的视网膜图像，分为 5 个 DR 阶段。许多图像质量很差，而且一些基本事实的标签也不正确。
• IDRID [21]（印度糖尿病视网膜病变图像数据集）包含 516 张视网膜眼底图像，由位于印度马哈拉施特拉邦南德的一家眼科诊所的视网膜专家拍摄。
• DIARETDBI [22] 包含 89 个大小的视网膜眼底图像1500∗1152像素，包括 5 个正常图像和所有其他 84 个 DR 图像。
• DDR[23]（糖尿病视网膜病变的诊断）——包含 13,637 张视网膜眼底图像，显示 DR 的五个阶段。从数据集中，757 张图像显示 DR 病变。
• 其他如 E-ophtha [24]、HRF [25]、ROC [26] 和 DR2 [27] 等。

## 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。

## 机器学习代写|深度学习project代写deep learning代考|DEEP LEARNING IN THE DETECTION OF DIABETIC RETINOPATHY

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

## 机器学习代写|深度学习project代写deep learning代考|DEEP LEARNING IN THE DETECTION OF DIABETIC RETINOPATHY

A few years back, we would have never thought of deep learning applications to develop virtual assistants (like Alexa, Siri, and Google Assistant) and self-driving cars. But now, these developments are part of

our daily lives. Deep learning is fascinating to us with its ongoing actions, such as fraud detection and pixel restoration.

Although deep learning is achieving quite impressive results in realworld applications, it is essential to note that it is not magic to achieve those results; large amounts of data are required. Furthermore, learning from this amount of data is a very time-consuming and computationally demanding process.

Nevertheless, it is terrible how these algorithms can “learn” without telling the model what to look for – it learns based on experience and the examples given. And as mentioned, progress in this area has been made to develop surprising and valuable applications that we will discuss next.
Deep Learning is a very vast field to discuss and do research on. There are hundreds of applications present at the moment that use deep learning methods. Hundreds of fascinating applications will come in the future, like MIT is working on future prediction using deep learning methods.

Deep learning is widely used in the medical field-computer vision techniques like image segmentation, image classification, etc. Using different deep learning architectures (such as CNN, RNN, LSTM) can detect any disease from other image datasets. Deep learning helps medical experts diagnose disease more accurately with minimum error and allows them to treat it better, thus leading to better decisions.

This chapter will discuss eye disease, which can cause blindness known as Diabetic Retinopathy (DR). Early detection of DR is a critical task, and deep learning helps in its early detection.

## 机器学习代写|深度学习project代写deep learning代考|Diabetic Retinopathy

Diabetes is a condition that occurs in the human body when the pancreas fails to produce the required insulin or when the body fails to process it properly. As it advances, it starts affecting the circulatory system of the human body, including the retina. It causes damage to the retinal blood vessels, leading to diabetic retinopathy by decreasing the

patient’s vision. This disease can cause permanent blindness to the affected person if appropriate treatment is not provided in the early stages.

The abnormal shift in blood sugar level starts happening in diabetes mellitus. Generally, glucose is converted into energy in the human body that helps to perform normal human functions. But in the worst-case scenario, there is an abnormal blood sugar level, and the excess blood sugar causes hyperglycemia. Non-proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) are two main stages of DR, as shown in Figure 2. NPDR is a condition in which the retina becomes inflamed (a case of macular edema) because of the accumulation of glucose that leads to leakage of blood vessels in the eyes. In a severe condition, retinal vessels might get blocked completely, which causes macular ischemia. There are different levels in NPDR in which sometimes the patient suffers from blurred vision or loses sight partially or entirely. PDR occurs in the advanced stage of diabetes, in which extra blood vessels start growing in the retina (a case of neovascularization). These new blood vessels are very narrow and brittle, tend to cause haemorrhages, and lead to partial or complete loss of vision.

## 机器学习代写|深度学习project代写deep learning代考|Severity Levels of DR

Early Treatment Diabetic Retinopathy Study Research Group (ETDRS) and International Clinical Diabetic Retinopathy (ICDR) [12] has given different levels of severity of DR defined as under:

• Level 0- No retinopathy.
• Level 1- Mild NPDR- the presence of at least one microaneurysm with or without other lesions.
• Level 2- Moderate NPDR- the presence of many microaneurysms and retinal haemorrhages with or without cotton wool spots.
• Level 3- Severe $N P D R$ – the presence of many haemorrhages and micro-aneurysms in four quadrants of the retina, cotton wool spots in two or more quadrants and Intra-retinal microvascular abnormalities in one or more quadrants.
• Level 4- PDR- it is an advanced stage of DR where new narrow and brittle or weak blood vessels are present with a high risk of leakage, and it can cause severe vision loss and sometimes even blindness. Figure 4 shows images of different levels of severity in DR.

## 机器学习代写|深度学习project代写deep learning代考|Severity Levels of DR

• 0 级 – 无视网膜病变。
• 1级-轻度NPDR-存在至少一个微动脉瘤，有或没有其他病变。
• 2 级 – 中度 NPDR – 存在许多微动脉瘤和视网膜出血，伴有或不伴有棉絮斑。
• 3级 – 严重ñ磷DR– 在视网膜的四个象限中存在许多出血和微动脉瘤，在两个或更多象限中存在棉绒斑，在一个或多个象限中存在视网膜内微血管异常。
• 4级-PDR-这是DR的晚期阶段，出现新的狭窄、脆弱或脆弱的血管，具有很高的渗漏风险，可能导致严重的视力丧失，有时甚至失明。图 4 显示了 DR 中不同严重程度的图像。

## 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。

## 机器学习代写|深度学习project代写deep learning代考|Long Short Term Memory

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

## 机器学习代写|深度学习project代写deep learning代考|Long Short Term Memory

LSTM is a difficult technique in deep learning to master. LSTM has feedback connections, unlike traditional feed-forward neural networks. It can process entire data sequences such as speech or video, as well as single data points such as images [8]. LSTM overcomes the problems of the RNN model. RNN model suffers from short-term memory. RNN model has no control over which part of the information needs to be carried forward and how many parts need to be forgotten. A memory unit called a cell is utilized by the LSTM which can maintain information for

a sufficient period. LSTM networks are a type of RNN that can learn long chains of dependencies. LSTM has different memory blocks called cell which carries information throughout the processing of the sequence. The two states that are input to the next cell are the cell state and the hidden state. Three major techniques, referred to as gates, are used to manipulate this memory. A typical LSTM unit consists of a cell or memory block, an input gate, an output gate, and a forget-gate. The information in the cell is regulated by the three gates, and the cell remembers values for arbitrary periods. This model contains interacting layers in a repeating module.

Forget-gate layer is responsible for what to keep and what to throw from old information. Data that isn’t needed in LSTM to comprehend the information of low significance is removed by multiplying a filter. This is mandated for the LSTM network’s effectiveness to be optimized.

The input gate layer manages of determining what data should be stored in the cell state. To control what values should be assigned to the cell state, a sigmoid function is used. In the same way that the forget-gate filters all the data, this one does as well. The cell state is only updated with information that is both important and not useless.

## 机器学习代写|深度学习project代写deep learning代考|ABSTRACT

Diabetic Retinopathy (DR) is one of the common issues of diabetic Mellitus that affects the eyesight of humans by causing lesions in their retinas. DR is mainly caused by the damage of blood vessels in the tissue of the retina, and it is one of the leading causes of visual impairment globally. It can even cause blindness if not detected in its early stages. To reduce the risk of eyesight loss, early detection and treatment are pretty necessary. The manual process by ophthalmologists in detection DR requires much effort and time and is costly also. Many computer-based techniques reduce the manual effort, and deep learning is used more commonly in medical imaging. This chapter will discuss deep learning and how it is helpful in the early detection and classification of DR by reviewing some latest state-of-art methods. There are various datasets of colour fundus images available publically, and we have reviewed those databases in this chapter.

## 机器学习代写|深度学习project代写deep learning代考|INTRODUCTION

The terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are usually used identically but are not the same. AI is called a vast field of research where the goal is to make the device interact with its nature as an intelligent person. Machine Learning (ML) is a subset of AI where a machine learns to perform a task without explicit programming. Deep learning (DL) is a subset or sub-field of ML that deals with algorithms that use deep neural networks.

DL (also called hierarchical learning or deep structured learning) is a part of machine learning that is based on some set of algorithms, which performs a high level of abstractions in data [1-4]. Such algorithms develop a layered and hierarchical architecture of learning, understanding, and representing the data. This advanced learning technology is inspired by artificial intelligence, which imitates the deep, layered learning process of the human brain, which automatically extracts features and releases primary data $[5,6]$. DL algorithms are useful as they can deal with large amounts of unsupervised data and naturally learn data representation in a deep layer-wise method which a simple ML algorithm can’t do $[7,8]$.

Applications of DL in today’s world are a comprehensive concept. Many deep learning architectures like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) have been implemented in areas like computer vision, speech recognition, NLP (Natural Language Processing), audio recognition and

bioinformatics, etc. [9]. Deep learning can be depicted as a class of machine learning algorithms that uses a cascade of multiple layers for feature extraction and transformation. Output from the previous layer is used as input for the next layer. Algorithms of deep learning can be both supervised or unsupervised [9].

The number of parameterized transformations is a signal encounter as it propagates from the input layer to the output layer and the number of hidden layers present in the network. In deep networks, processing units with trainable parameters, like weights and thresholds, are the parameterized transformations. Figure 1 shows the difference between these two networks. A chain of transformations between the input and output layers is the credit assignment path (CAP), which may vary in length and defines connections between input and output.

## 机器学习代写|深度学习project代写deep learning代考|Long Short Term Memory

LSTM 是一种很难掌握的深度学习技术。与传统的前馈神经网络不同，LSTM 具有反馈连接。它可以处理整个数据序列，例如语音或视频，以及单个数据点，例如图像 [8]。LSTM 克服了 RNN 模型的问题。RNN 模型存在短期记忆。RNN 模型无法控制哪些部分的信息需要被继承，多少部分需要被遗忘。LSTM 使用称为单元的存储单元，它可以保存信息

Forget-gate 层负责从旧信息中保留什么以及丢弃什么。通过乘以一个过滤器来删除 LSTM 中不需要理解低重要性信息的数据。这是为了优化 LSTM 网络的有效性而强制要求的。

## 机器学习代写|深度学习project代写deep learning代考|INTRODUCTION

DL（也称为分层学习或深度结构化学习）是机器学习的一部分，它基于一组算法，在数据中执行高级抽象 [1-4]。此类算法开发了一种分层和分层的架构，用于学习、理解和表示数据。这种先进的学习技术受到人工智能的启发，它模仿人脑的深层、分层学习过程，自动提取特征并释放原始数据[5,6]. DL 算法很有用，因为它们可以处理大量无监督数据，并以简单的 ML 算法无法做到的深层方法自然地学习数据表示[7,8].

DL 在当今世界的应用是一个综合概念。许多深度学习架构，如深度神经网络 (DNN)、卷积神经网络 (CNN) 和循环神经网络 (RNN)，已在计算机视觉、语音识别、NLP（自然语言处理）、音频识别和

## 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。

## 机器学习代写|深度学习project代写deep learning代考|DEEP LEARNING BASED APPROACHES FOR TEXT RECOGNITION

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

## 机器学习代写|深度学习project代写deep learning代考|HyShalini Agrahari* and Arvind Kumar Tiwari

Text recognition has risen in popularity in the area of computer vision and natural language processing due to its use in different fields. For character recognition in a handwriting recognition system, several methods have been suggested. There are enough studies that define the techniques for translating text information from a piece of paper to an electronic format. Text recognition systems may play a key role in creating a paper-free environment in the future by digitizing and handling existing paper records. This chapter provides a thorough analysis of the field of Text Recognition.

We are all familiar with the convenience of having an editable text document that can be easily read by a computer and the information can be used for a variety of uses. People always wanted to use the text that is present in various forms all around them, such as handwritten documents, receipts, images, signboards, hoardings, street signs, nameplates, number plates of automobiles, as subtitles in videos, as captions for photos, and in a variety of other ways. However, we are unable to make use of this information because our computer is unable to recognize these texts purely based on their raw images. Hence, researchers around the world have been trying hard to make computers worthy of directly recognizing text by acquiring images to use the several information sources that could be used in a variety of ways by our computers. In most cases, we have no choice but to typewrite handwritten information, which is very timeconsuming. So, here we have a text recognition system that overcomes these problems. We can see the importance of a ‘Text Recognition System’ just by having to look at these scenarios, which have a wide range of applications in security, robotics, official documentation, content filtering, and many more.

Due to digitalization, there is a huge demand for storing data into the computer by converting documents into digital format. It is difficult to recognize text in various sources like text documents, images, and videos, etc. due to some noise. The text recognition system is a technique by which recognizer recognizes the characters or texts or various symbols. The text recognition system consists of a procedure of transforming input images into machine-understandable format $[1,2]$.

The use of text recognition has a lot of benefits. For example, we find a lot of historical papers in offices and other places that can be easily replaced with editable text and archived instead of taking up too much space with their hard copies. Online and offline text recognition are the two main types of recognition whether online recognition system includes tablet and digital pen, while offline recognition includes printed or handwritten documents [3].

## 机器学习代写|深度学习project代写deep learning代考|Convolutional Neural Network

CNN [6] is a method of deep learning algorithm that is specifically trained to perform with image files. A simple class that perfectly represents the image in CNN, processed through a series of convolutional layers, a pooling layer, and fully connected layers. CNN can learn multiple layers of feature representations of an image by applying

different techniques. Low-level features such as edges and curves are examined by image classification in this method and a sequence of convolutional layers helps in building up to more abstract. CNN provides greater precision and improves performance because of its exclusive characteristics, such as local connectivity and parameter sharing. The input layer, multiple hidden layers (convolutional, normalization, pooling), and a fully connected and output layer make up the system of CNN. Neurons in one layer communicate with some neurons in the next layer, making the scaling simpler for higher resolutions.

In the input layer, the input file is recorded and collected. This layer contains information about the input image’s height, width, and several channels (RGB information). To recognize the features, the network will use a sequence of convolutions and pooling operations in multiple hidden layers. Convolution is one of the most important components of a CNN. The numerical mixture of multiple functions to produce a new function is known as convolution. Convolution is applied to the input image via a filter or, to produce a feature map in the case of a CNN. The input layer contains $n \times n$ input neurons which are convoluted with the filter size of $m$ $\times m$ and return output size of $(n-m+1) \times(n-m+1)$. On our input, we perform several convolutions, each with a different filter. As a result, different feature maps emerge. Finally, we combine these entire feature maps to create the convolution layer final output. To reduce the input feature space and hence reduces the higher computation; a pooling layer is placed between two convolutional layers. Pooling allows passing only the values you want to the next layer, leaving the unnecessary behind. This reduces training time, prevents overfitting, and helps in feature selection. The max-pooling operation takes the highest value from each sub-region of the image vector while keeping the most information, this operation is generally preferred in modern applications. CNN’s architecture, like regular neural network architecture, includes an activation function to present non-linearity into the system. Among the various activation functions used extensively in deep learning models, the sigmoid function rectified linear unit (ReLu), and softmax are some wellknown examples. In CNN architecture, the classification layer is the final

layer. It’s a fully connected feed-forward network that’s most commonly used as a classifier. This layer determines predicted classes by categorizing the input image, which is accomplished by combining all the previous layers’ features.

Image recognition, image classification, object detection, and face recognition are just a few of the applications for CNN. The most important section in CNN is the feature extraction section and classification section.

## 机器学习代写|深度学习project代写deep learning代考|Recurrent Neural Network

RNN is a deep learning technique that is both effective and robust, and it is one of the most promising methods currently in use because it is the only one with internal storage. RNN is useful when it is required to predict the next word of sequence [7]. When dealing with sequential data (financial data or the DNA sequence), recurrent neural networks are commonly used. The reason for this is that the model employs layers, which provide a short-term memory for the model. Using this memory, it can more accurately determine the next data and memorize all the information about what was calculated. If we want to use sequence matches in such data, we’ll need a network with previous knowledge of the data. The output from the previous step is fed into the current step in this approach. The architecture of RNN includes three layers: input layer, hidden layer, and output layer. The hidden layer remembers information about sequences.

If compare RNN with a traditional feed-forward neural network(FNN), FNN cannot remember the sequence of data. Suppose we give a word “hello” as input to FNN, FNN processes it character by character. It has already forgotten about ‘ $h$ ‘ ‘e’ and ‘ $l$ ‘ by the time it gets to the character ‘ $o$ ‘. Fortunately, because of its internal memory, a recurrent neural network can remember those characters. This is important because the data sequence comprises important information about what will happen next, that’s why an RNN can perform tasks that some other techniques cannot.

## 机器学习代写|深度学习project代写deep learning代考|Convolutional Neural Network

CNN [6] 是一种深度学习算法，专门训练用于处理图像文件。一个简单的类，完美地表示 CNN 中的图像，通过一系列卷积层、一个池化层和全连接层进行处理。CNN 可以通过应用来学习图像的多层特征表示

## 机器学习代写|深度学习project代写deep learning代考|Recurrent Neural Network

RNN 是一种既有效又健壮的深度学习技术，它是目前使用的最有前途的方法之一，因为它是唯一具有内部存储的方法。当需要预测序列的下一个单词时，RNN 很有用 [7]。在处理顺序数据（财务数据或 DNA 序列）时，通常使用循环神经网络。原因是模型使用了层，这些层为模型提供了短期记忆。使用此内存，它可以更准确地确定下一个数据并记住有关计算内容的所有信息。如果我们想在此类数据中使用序列匹配，我们需要一个具有数据先前知识的网络。在这种方法中，上一步的输出被馈送到当前步骤。RNN的架构包括三层：输入层，隐藏层和输出层。隐藏层记住有关序列的信息。

## 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。

## 机器学习代写|深度学习project代写deep learning代考|Hybrid Recommendation System Based on Deep Neural Networks

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

## 机器学习代写|深度学习project代写deep learning代考|Hybrid Recommendation System Based on Deep Neural Networks

The state-of-the-art Collaborative Filtering-based approach uses the ranking matrix to suggest products. But this method suffers from a data sparsity and a cold start crisis. Due to the scarce existence of the useritem matrix, the features learned are not accurate, which decreases the efficiency of the recommendation framework. Hybrid recommendation model based on deep neural networks incorporates a content-based recommendation model with collective recommendation-based filtering models, which combines the mechanism of feature learning and recommendation into a single model.
[25] suggested a layered, self-encoder-based hybrid paradigm that learns the latent space factors of users and items and simultaneously performs mutual filtering from the ranking matrix.

An autoencoder is a variation of neural organization, having encoder and decoder as two components. The encoder changes over the contribution to its secret portrayal, while the decoder changes over the secret portrayal back to the re-established input structure. The boundaries comparing to the autoencoder are prepared to limit the mistake because of the recreation, which is estimated by the loss work or misfortune work. Denoising autoencoder (DAE) attempts to recreate the contribution from a ruined adaptation for improved portrayal from the info. More variations of autoencoder have been created for better results. The crossover suggestion model dependent on the stacked denoising autoencoder utilizes both the rating network and the side data and coordinates both the SDAE [26] and the grid factorisation. Lattice factorization is a broadly utilized model with improved exactness, and SDAE is an incredible model for separating significant level highlights from inputs. The combination of the over two model will turn into an amazing model for additional advantages.

## 机器学习代写|深度学习project代写deep learning代考|Social Network-Based Recommendation System Using Deep Neural Networks

Conventional recommendation models never consider social connections among the users. But we always take verbal recommendations from our friends in reality.These verbal recommendations are termed as social recommendation which occurs daily [27]. Hence, for improved recommendation systems and for more personalized recommendations, social network must be employed among users. Every user will interact with various types of social relationships.
The quality of recommendation system is very crucial task which can be achieved by implementing the effect of social relationship among the users. Items with location attributes and sequential pattern of user behaviour in spatial and temporal frame are used to form spatiotemporal pattern which is used to improve recommendation accuracy. Recently, a very few recommendation techniques have been proposed which is based on the users’ trust relations improve conventional recommendation systems. These trust based recommendation models proves to be an effective move in the field of recommendation system models.

In current scenario an integration of deep learning and social network based recommendation system provides a platform for various research solutions. The limitations which are inherent to the social recommendation must be addressed in the future research.

## 机器学习代写|深度学习project代写deep learning代考|Context-Aware Recommendation Systems Based on Deep Neural Networks

A context-aware recommendation system, integrates context based information into a recommendation model. This integration is effectively performed by the deep learning techniques in different conditions of recommending items [28]. Deep neural network based methods are used to extract the latent space presentation from the context based information. Deep learning based model can be integrated into diverse data to reduce data sparsity problem [29].

Sequential nature of data plays a significant part in implementing user behaviours. Recently, recurrent neural networks (RNNs) are commonly used in a variety of sequential simulation activities. However, for real-world implementations, these approaches have trouble modeling contextual knowledge, which has been shown to be very essential for behavioural modelling.

Currently this method based on deep neural networks focused towards situation information. A novel approach is proposed called context-aware recurrent neural networks. It uses two types of matrices: input and transition matrices. They both are specific to the context and adaptive in nature. Input matrices are used to extract various situations such as time, place, weather condition where actually the user behaves.

As deep learning plays a significant role in most of the fields as it has the capability of dealing with large and complex problems with improved results. Deep learning technology also contributes in the field of recommendation system for improved customer satisfaction. Deep learning technology overcomes the shortcomings of traditional models to get high quality recommendations. [30].

All the above discussed methods of recommendation systems use deep neural networks and hence also achieve the quality in recommending items to the users [31]. Different recommendation models use different deep learning methods to obtain improve results.

## 机器学习代写|深度学习project代写deep learning代考|Hybrid Recommendation System Based on Deep Neural Networks

[25] 提出了一种分层的、基于自编码器的混合范式，它学习用户和项目的潜在空间因子，同时从排名矩阵执行相互过滤。

## 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。