### 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

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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。