# 线性代数网课代修|机器学习代写 machine learning代考|CS234

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• 数值分析
• 高等线性代数
• 矩阵论
• 优化理论
• 线性规划
• 逼近论

## 线性代数作业代写linear algebra代考|MACHINE AND DEEP LEARNING ALGORITHMS

Machine learning algorithms assist people and animals to learn as natural. These algorithms are used to study the raw data to your computer processes, while the number of trails available to learning performance without adopting a pre-set model machine. These models can recognize natural phenomena by giving insight and help to make more informed major decisions like forecasts, medical diagnosis, inventory, trading, energy consumption assessment, and more. Media sites concentrate on the teaching of computers and millions of possible ways to propose movies or songs. This is being achieved by businesses to listen to their consumers’ shopping patterns. It includes two approaches: supervised learning, which builds a framework with knowing input and output data to forecast future results, and unsupervised learning, including input information with hidden patterns or intrinsic structures.

Up to this point, the author has indicated various types of machine learning and many of these algorithms will have their advantages and limitations, which make them perfect for use in such fields. Enhanced versions of such algorithms are defined as deep learning algorithms, which have become extremely popular lately. These approaches, though, come from biological neural networks that date back many years ago, before their contribution to data science. The author has used simply machine and deep learning algorithms in this research, and some of its most widely used LIDCIDRI datasets for the research [Pradhan et al., 2020]. The output analyses, as well as the findings of this deep study, will assist researchers and specialists in similar fields to gain improved information and experience to create the best algorithms in line with their challenges in this notable number of algorithms based on data of different lung cancer datasets. Logistical Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive (GNB), Decision Trees (DT), Random Forestry (RF) are the machine learning algorithms discussed in this chapter. As the leading category of artificial neural networks in deep learning, the Bidirectional Long Term Memory (BLSTM) and Long Short Term Memory (LSTM) are analyzed.

## 线性代数作业代写linear algebra代考|Bidirectional LONG SHORT-Term MeMORY Networks

Since the mid-twentieth century, the release of the long short-term memory (LSTM) [Zhang et al., 2018] would have rendered artificial neural networks Kumar et al., 2012, a remarkably recurrent neural network (RNN). Through its straight ties in the groupings, RNN may become dependent on time. To demonstrate this, feed-forward networks should implement the use of static pattern mapping, e.g. MLP.

Moreover, traditional RNNs [Wang Ran et al., 2018] became responsive, particularly in descending gradient preparation, to this extremely genuine issue, including the issue of the disappearing gradient. This issue arises hecause the gradient disappears or explodes exponentially. Therefore, after several steps, traditional RNNs “forget” interest points fewer than ten times after missing gradients. The impact may be unusually restricted towards the latter research methods, as well as the linkage and connection of progressively segregated preceding layers being very complicated and problematic.

The issue expertly illuminated by LSTM [Zhang et al., 2018] outlines itself as a special repeating arrangement that can see like differentiable capacity cells. A mistake inside the LSTM block, which can be maintained for long periods, which can be maintained at an intersection for a long period. LSTMs can oversee time-laps of up to 10,000 steps [Salehi et al., 2018] and for exceptionally long periods. LSTM [Salman et al., 2020] discharges a surprising capacity to learn from other abilities in repeating sets, e.g. precise timing, exact replication esteem, expansion, and now and then increase [Albu et al. 2019]. Analysts have used an angle to learn measurements of LSTMs from back-propagation time [Mhaske Diksha et al., 2019].

## 线性代数作业代写linear algebra代考|Bidirectional LONG SHORT-Term MeMORY Networks

LSTM [Zhang et al., 2018] 熟练地阐明了这个问题，将其自身描述为一种特殊的重复排列，可以看到类似可微分容量的电池。LSTM块内部的一个错误，可以长期维护，在路口可以长期维护。LSTM 可以监督多达 10,000 步的时间间隔 [Salehi et al., 2018] 和非常长的时间段。LSTM [Salman et al., 2020] 释放出惊人的能力，可以从重复集中的其他能力中学习，例如精确的时间、精确的复制价值、扩展，以及不时增加 [Albu et al. 2019]。分析师已经使用一个角度从反向传播时间学习 LSTM 的测量值 [Mhaske Diksha et al., 2019]。

# 计量经济学代写

## 在这种情况下，如何学好线性代数？如何保证线性代数能获得高分呢？

1.1 mark on book

【重点的误解】划重点不是书上粗体，更不是每个定义，线代概念这么多，很多朋友强迫症似的把每个定义整整齐齐用荧光笔标出来，然后整本书都是重点，那期末怎么复习呀。我认为需要标出的重点为

A. 不懂，或是生涩，或是不熟悉的部分。这点很重要，有的定义浅显，但证明方法很奇怪。我会将晦涩的定义，证明方法标出。在看书时，所有例题将答案遮住，自己做，卡住了就说明不熟悉这个例题的方法，也标出。

B. 老师课上总结或强调的部分。这个没啥好讲的，跟着老师走就对了

C. 你自己做题过程中，发现模糊的知识点

1.2 take note

1.3 understand the relation between definitions