机器学习代写|深度学习project代写deep learning代考|APPLICATION OF DEEP LEARNING

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深度学习是机器学习的一个子集,它本质上是一个具有三层或更多层的神经网络。这些神经网络试图模拟人脑的行为–尽管远未达到与之匹配的能力–允许它从大量数据中 “学习”。

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。

我们提供的深度学习deep learning及其相关学科的代写,服务范围广, 其中包括但不限于:

  • 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
机器学习代写|深度学习project代写deep learning代考|APPLICATION OF DEEP LEARNING

机器学习代写|深度学习project代写deep learning代考|IN RECOMMENDATION SYSTEM

DL techniques are widely used in various real world applications such as sentiment analysis, speech recognition image classification, text classification etc. Various researchers also include deep neural network based techniques in the field of recommendation system to improve its performance as compared to traditional recommendations systems. Traditional recommendation methods use matrix factorization methods which have some limitations such as:

  1. The trouble of utilizing side highlights that may influence the suggestion like the U/PG rating of a film or the nation of the client. We can just utilize the thing ID and the client ID on account of network factorization. It likewise keeps us from questioning a thing or client not present in the preparation set.
  2. The matrix factorization additionally had the virus start issue because of the way that it had no component vector or inserting for the new things.
  3. Matrix factorization regularly will in general prescribe mainstream things to everybody which doesn’t generally mirror the particular client interests for the most part when Dot items are utilized.
  4. Matrix factorization deals with the straightforward internal result of the User and thing highlight embeddings, it is regularly insufficient to catch and address the mind boggling relations in the client and things.

Deep Neural networks are planned and used to address these weaknesses of the matrix factorization strategies. This section describes the various categories recommendation systems which are based on deep learning.

The categorization is based on the types of recommendation systems used which is as follows:

  1. Collaborative filtering recommendation system based on deep neural network.
  2. Content-based recommendation system based on deep neural network.
  3. Hybrid recommendation system based on deep neural network.
  4. Social network-based recommendation system based on deep neural network.
  5. Context aware recommendation system based on deep neural network.

Hybrid model and neural network model are the two categories of deep neural network-based recommendation system. Integration model is further divided into two categories on the basis of whether it combines any traditional recommendation system model with deep neural network technique or depends solely on deep learning method.

Neural network model is also divided into two categories on the basis of deep neural network based technique used: models which uses single deep neural network based technique and deep neural network based composite model. In deep neural network based composite model, different deep neural network techniques are used to build a hybrid system having more capability.

机器学习代写|深度学习project代写deep learning代考| Collaborative Filtering Method Based on Generative Adversarial Network

Generative Adversarial Network is a neural network which is generative and having discriminator and generator functions. These both functions are simultaneously trained in competition with one another in architecture of minimax game. The first model to implement GAN in the field of Information Retrieval is (IRGAN) [18] which stands for Information retrieval generative adversarial network. The state of the art GAN model has two modules a discriminator and a generator. The generative retrieval module predicts appropriate documents with given query, whereas discriminative retrieval module predicts relevancy given with a pair of query and document.

The IRGAN model combines above two Information Retrieval models in order to play a minimax game with them: the generative retrieval model produces (or selects) relevant documents that are relevant documents like ground truth, while the discriminating retrieval model separates the relevant documents from those generated by the generative retrieval model [32]. concentration is on the semantic-rich client thing communications in a recommender framework and propose a novel generative adversarial network (GAN) named Convolutional Generative Collaborative Filtering (Conv-GCF). They build up a powerful irritation system (ill-disposed commotion layer) for convolutional neural organizations (CNN), in light of which a generator is planned with lingering squares to combine client thing collaborations.

机器学习代写|深度学习project代写deep learning代考|Recurrent Neural Network Based Collaborative Filtering Method

In order to deal with the information in sequential form, recurrent neural network (RNN) proves to be a very effective network. Concepts of loops are used in place of feedforward network to remember sequences. Variants of RNN viz. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) network are used to deal with the problems of long term dependencies and vanishing gradient problem. In collaborative filtering method based on RNN, the impact of user historical pattern is modelled on the current behavior of user, recommendation is performed and user’s behavior is predicted [19]. Figure 7 shows the framework of collaborative filtering method based on RNN [19]. Let the input set is $\left{I_{l}\right.$, $\left.I_{2} \ldots I_{t}\right}$, and output is $O_{t}=\sigma\left(f\left(W \cdot h_{t-1}+V \cdot I_{t}\right) \cdot V\right), \sigma$ represents a softmax function, $f$ represents the activation function, which specifies the selection probability of any item at time $t$. $h_{t}$ represents the hidden state vector.

The first ever developed autoencoder-based collaborative recommendation model is Autoencoder based Collaborative filtering [20]. It decomposes the vectors by integer ratings. The model proposed by [20] takes client or thing based evaluations as contributions to rating matrix R. The output is produced by the process of encoding and decoding by optimizing the parameters of model and reducing the reconstruction error. Consider an example, if the range of integers [1-5] represents the rating score, then each $r_{u i}$ can be divided into five vectors.
Above figure represents the 1 to 5 rating scale in which blue boxes represents the user rated item. The cost function which is to be reduced is taken as Mean Square Error. The rating prediction in this approach is found by making the summary of each of the five vectors, which are scaled by rating $\mathrm{K}$. Pretraining of parameters and local optimum avoidance is performed by RBM. Stacking multiple autoencoder collectively shows the slight improvement in accuracy. This method based on autoencoder suffers from the problem of dealing with noninteger ratings and sparseness of input data due to decomposition of partial observed vectors.

Collaborative Denoising Auto-Encoder [21] is primarily used for prediction rankings. User feedback is taken as input to the CDAE. If the user enjoys a movie, the input value is 1 otherwise it is 0 . It shows the vector preference to display the user’s interest in some item. Gaussian noise corrupts the CDAE input.

机器学习代写|深度学习project代写deep learning代考|APPLICATION OF DEEP LEARNING


机器学习代写|深度学习project代写deep learning代考|IN RECOMMENDATION SYSTEM

DL 技术广泛用于各种现实世界的应用,例如情感分析、语音识别图像分类、文本分类等。各种研究人员还将基于深度神经网络的技术纳入推荐系统领域,以提高其与传统推荐系统相比的性能。传统的推荐方法使用矩阵分解方法,这些方法具有一些局限性,例如:

  1. 使用可能会影响建议的侧面亮点的麻烦,例如电影的 U/PG 评级或客户所在的国家/地区。由于网络分解,我们可以只使用事物 ID 和客户端 ID。它同样可以防止我们质疑准备集中不存在的事物或客户。
  2. 矩阵分解还存在病毒启动问题,因为它没有分量向量或插入新事物。
  3. 矩阵分解通常会给每个人开出主流的东西,当使用 Dot 项目时,这通常不会反映特定客户的兴趣。
  4. 矩阵分解处理用户和事物突出嵌入的直接内部结果,通常不足以捕捉和解决客户端和事物中令人难以置信的关系。



  1. 基于深度神经网络的协同过滤推荐系统。
  2. 基于深度神经网络的基于内容的推荐系统。
  3. 基于深度神经网络的混合推荐系统。
  4. 基于深度神经网络的基于社交网络的推荐系统。
  5. 基于深度神经网络的上下文感知推荐系统。



机器学习代写|深度学习project代写deep learning代考| Collaborative Filtering Method Based on Generative Adversarial Network

Generative Adversarial Network 是一种具有生成性并具有鉴别器和生成器功能的神经网络。在极小极大游戏的架构中,这两个功能在相互竞争中同时进行训练。在信息检索领域实现 GAN 的第一个模型是(IRGAN)[18],它代表信息检索生成对抗网络。最先进的 GAN 模型有两个模块:鉴别器和生成器。生成检索模块预测具有给定查询的适当文档,而判别检索模块预测给定查询和文档对的相关性。

IRGAN 模型结合了上述两个信息检索模型,以便与它们进行极小极大游戏:生成检索模型生成(或选择)相关文档,这些文档是相关文档,如基本事实,而判别检索模型将相关文档与生成的文档分开由生成检索模型[32]。专注于推荐框架中语义丰富的客户端事物通信,并提出了一种名为卷积生成协同过滤(Conv-GCF)的新型生成对抗网络(GAN)。他们为卷积神经组织 (CNN) 建立了一个强大的刺激系统(不适当地的骚动层),据此计划了一个带有挥之不去的正方形的生成器,以结合客户事物的协作。

机器学习代写|深度学习project代写deep learning代考|Recurrent Neural Network Based Collaborative Filtering Method

为了以顺序形式处理信息,循环神经网络(RNN)被证明是一种非常有效的网络。循环的概念用于代替前馈网络来记住序列。RNN 的变体,即。长短期记忆(LSTM)和门控循环单元(GRU)网络用于处理长期依赖和梯度消失问题。在基于RNN的协同过滤方法中,用户历史模式对用户当前行为的影响进行建模,进行推荐并预测用户行为[19]。图 7 显示了基于 RNN [19] 的协同过滤方法的框架。设输入集为\left{I_{l}\right.$, $\left.I_{2} \ldots I_{t}\right}\left{I_{l}\right.$, $\left.I_{2} \ldots I_{t}\right},输出为这吨=σ(F(在⋅H吨−1+在⋅一世吨)⋅在),σ表示一个softmax函数,F表示激活函数,它指定了任何一个项目在时间的选择概率吨. H吨表示隐藏状态向量。

有史以来第一个开发的基于自动编码器的协作推荐模型是基于自动编码器的协作过滤[20]。它通过整数评级分解向量。[20] 提出的模型将基于客户或事物的评估作为对评分矩阵 R 的贡献。输出是通过优化模型参数和减少重构误差的编码和解码过程产生的。考虑一个例子,如果整数范围 [1-5] 代表评分,那么每个r在一世可以分为五个向量。
上图代表 1 到 5 的评分等级,其中蓝色框代表用户评分项目。要减少的成本函数被视为均方误差。这种方法中的评分预测是通过对五个向量中的每一个进行汇总来找到的,这些向量按评分进行缩放ķ. RBM 执行参数的预训练和局部最优避免。堆叠多个自动编码器共同显示准确性略有提高。这种基于自编码器的方法由于部分观测向量的分解而存在处理非整数评级和输入数据稀疏的问题。

协作去噪自动编码器[21]主要用于预测排名。用户反馈作为 CDAE 的输入。如果用户喜欢电影,则输入值为 1 ,否则为 0 。它显示了向量偏好以显示用户对某些项目的兴趣。高斯噪声会破坏 CDAE 输入。

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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


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。



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