## 机器学习代写|自然语言处理代写NLP代考|CS4650

2022年7月5日

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• 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
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## 机器学习代写|自然语言处理代写NLP代考|The human intelligence stack

On the left side of Figure 4.1, we can see that the input for humans is the perception of raw events for layer 0 , and the output is language. We first perceive events with our senses as children. Gradually the output becomes burbling language and then structured language.
For humans, transduction goes through a trial-and-error process. Transduction means that we take structures we perceive and represent them with patterns, for example. We make representations of the world that we apply to our inductive thinking. Our inductive thinking relies on the quality of our transductions.

For example, as children, we were often forced to take a nap early in the afternoon. Famous child psychologist Piaget found that this could lead to some children saying, for example, “I haven’t taken a nap, so it’s not the afternoon.” The child sees two events, creates a link between them with transduction, and then makes an inference to generalize and make an induction.
At first, humans notice these patterns through transduction and generalize them through inductions. We are trained by trial and error to understand that many events are related:

Trained_related events $={$ sunrise – light,sunset $-$ dark, dark clouds – rain, blue sky running, food – good, fire – warm, snow – cold $}$

Over time, we are trained to understand millions of related events. New generations of humans did not have to start from scratch. They were only fine-tuned for many tasks by previous generations. They were taught that “fire burns you,” for example. From there on, a child knew that this knowledge could be fine-tuned to any form of “fire”: candles, wildfires, volcanoes, and every instance of “fire.”
Finally, humans transcribed everything they knew, imagined, or predicted into written language. The output of layer 0 was born.
For humans, the input of the next layer, layer 1 , is the vast amount of trained and fined-tuned knowledge. On top of that, humans perceive massive amounts of events that then go through the transduction, induction, training, and fine-tuning sub-layers along with previous transcribed knowledge.

Our infinite approach loop goes from layer 0 to layer 1 and back to layer 0 with more and more raw and processed information.

The result is fascinating! We do not need to learn (training) our native language from scratch to acquire summarization abilities. We use our pretrained knowledge to adjust (fine-tune) to summarization tasks.
Transformers go through the same process but in a different way.

## 机器学习代写|自然语言处理代写NLP代考|The machine intelligence stack

On the right side of Figure 4.1, we can see that the input for machines is second-hand information in the form of language. Our output is the only input machines have to analyze language.

At this point in human and machine history, computer vision identifies images but does not contain the grammatical structure of language. Speech recognition converts sound into words, which brings us back to written language. Music pattern recognition cannot lead to objective concepts expressed in words.
Machines start with a handicap. We impose an artificial disadvantage on them. Machines must rely on our random quality language outputs to:

• Perform transductions connecting all the tokens (sub-words) that occur together in language sequences
• Build inductions from these transductions
• Train those inductions based on tokens to produce patterns of tokens
Let’s stop at this point and peek into the process of the attention sub-layer, which is working hard to produce valid inductions:
• The transformer model excluded the former sequence-based learning operations and used self-attention to heighten the vision of the model
• Attention sub-layers have an advantage over humans at this point: they can process millions of examples for their inductive thinking operations
• Like us, they find patterns through transduction and induction
• They memorize these patterns with parameters that are stored with their model
• They have acquired language understanding by using their abilities: substantial data volumes, excellent NLP transformer algorithms, and computer power When the transformer model reaches the fine-tuning sub-layer of machine intelligence, it reacts like us. It does not start training from scratch to perform a new task. Like us, it considers it as a downstream task that only requires fine-tuning. If it needs to learn how to answer a question, it does not start learning a language from scratch. A transformer model just fine-tunes its parameters like us.

## 机器学习代写|自然语言处理代写NLP代考|The machine intelligence stack

• 执行连接在语言序列中一起出现的所有标记（子词）的转导
• 从这些转导中建立感应
• 训练那些基于标记的归纳以产生标记模式
让我们在这一点上停下来，看看注意力子层的过程，它正在努力产生有效的归纳：
• Transformer模型排除了之前基于序列的学习操作，使用self-attention来提升模型的视觉
• 在这一点上，注意力子层比人类有一个优势：他们可以处理数百万个例子来进行归纳思维操作
• 像我们一样，他们通过转导和感应找到模式
• 他们使用与模型一起存储的参数来记忆这些模式
• 他们通过自己的能力获得了语言理解：大量的数据量、出色的 NLP 转换器算法和计算机能力 当转换器模型到达机器智能的微调子层时，它会像我们一样做出反应。它不会从头开始训练来执行新任务。和我们一样，它认为它是一个只需要微调的下游任务。如果它需要学习如何回答问题，它不会从头开始学习一门语言。变压器模型只是像我们一样微调它的参数。

## 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。