## 机器学习代写|自然语言处理代写NLP代考|CS11-711

2022年7月5日

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## 机器学习代写|自然语言处理代写NLP代考|Running downstream tasks

In this section, we will just jump into some transformer cars and drive them around a bit to see what they do. There are many models and tasks. We will run a few of them in this section. Once you understand the process of running a few tasks, you will quickly understand all of them. After all, the human baseline of all of these tasks is us!
A downstream task is a fine-tuned transformer task that inherited the model and parameters from a pretrained transformer model.

A downstream task is thus the perspective of a pretrained model running finetuned tasks. That means, depending on the model, a task is downstream if it wasn’t used to fully pretrain the model. In this section, we will consider all of the tasks as downstream since we did not pretrain them.

Models will evolve, as will databases, benchmark methods, accuracy measurement methods, and leaderboard criteria. But the structure of human thought reflected through the downstream tasks in this chapter will remain.

## 机器学习代写|自然语言处理代写NLP代考|Machine Translation with the Transformer

Humans master sequence transduction, transferring a representation to another object. We can easily imagine a mental representation of a sequence. If somebody says, “The flowers in my garden are beautiful,” we can easily visualize a garden with flowers in it. We see images of the garden, although we might never have seen that garden. We might even imagine chirping birds and the scent of flowers.

A machine has to learn transduction from scratch with numerical representations. Recurrent or convolutional approaches have produced interesting results but have not reached significant BLEU translation evaluation scores. Translating requires the representation of language $A$ transposed into language $B$.
The Transformer model’s self-attention innovation increases the analytic ability of machine intelligence. A sequence in language $A$ is adequately represented before attempting to translate it into language $B$. Self-attention brings the level of intelligence required by a machine to obtain better BLEU scores.
The seminal “Attention Is All You Need” Transformer obtained the best results for English-German and English-French translations in 2017. Since then, the scores have been improved by other transformers.

At this point in the book, we have covered the essential aspects of transformers: the architecture of the Transformer, training a RoBERTa model from scratch, fine-tuning a BERT, evaluating a fine-tuned BERT, and exploring downstream tasks with some transformer examples.

In this chapter, we will go through machine translation in three additional topics. We will first define what machine translation is. We will then preprocess a WMT dataset. Finally, we will see how to implement machine translations.
This chapter covers the following topics:

• Defining machine translation
• Human transduction
• Machine transduction
• Preprocessing a WMT dataset
• Evaluating machine translation with BLEU
• Geometric evaluations
• Chencherry smoothing
• Enabling eager execution
• Initializing the English-German problem with Trax
Our first step will be to define machine translation.

## 机器学习代写|自然语言处理代写NLP代考|Machine Translation with the Transformer

Transformer 模型的自注意力创新增加了机器智能的分析能力。语言序列一个在尝试将其翻译成语言之前已充分表达乙. Self-attention 带来了机器获得更好的 BLEU 分数所需的智能水平。

• 定义机器翻译
• 人类转导
• 机器换能
• 预处理 WMT 数据集
• 使用 BLEU 评估机器翻译
• 几何评估
• 陈樱桃平滑
• 启用急切执行
• 使用 Trax 初始化英语-德语问题
我们的第一步是定义机器翻译。

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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。