机器学习代写|tensorflow代写| TensorFlow

TensorFlow是一个用于机器学习和人工智能的免费和开源的软件库。它可以用于一系列的任务，但特别关注深度神经网络的训练和推理。

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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

机器学习代写|tensorflow代写|TensorFlow

Google open-sourced its machine-learning framework, TensorFlow, in late 2015 under the Apache $2.0$ license. Before that, it was used proprietarily by Google in speech recognition, Search, Photos, and Gmail, among other applications.

The library is implemented in $\mathrm{C}++$ and has a convenient Python API, as well as a lessappreciated C++ API. Because of the simpler dependencies, TensorFlow can be quickly deployed to various architectures.

Similar to Theano-a popular numerical computation library for Python that you may be familiar with (http:// deeplearning.net/software/theano)-computations are described as flowcharts, separating design from implementation. With little to no hassle, this dichotomy allows the same design to be implemented on mobile devices as well as large-scale training systems with thousands of processors. The single system spans a broad range of platforms. TensorFlow also plays nicely with a variety of newer, similarly-developed ML libraries, including Keras (TensorFlow $2.0$ is fully integrated

with Keras), along with libraries such as PyTorch (https://pytorch.org), originally developed by Facebook, and richer application programming interfaces for ML such as Fast.Ai. You can use many toolkits to do ML, but you’re reading a book about TensorFlow, right? Let’s focus on it!

One of the fanciest properties of TensorFlow is its automatic differentiation capabilities. You can experiment with new networks without having to redefine many key calculations.
NOTE Automatic differentiation makes it much easier to implement backpropagation, which is a computationally-heavy calculation used in a branch of machine learning called neural networks. TensorFlow hides the nitty-gritty details of backpropagation so you can focus on the bigger picture. Chapter 11 covers an introduction to neural networks with TensorFlow.
All the mathematics is abstracted away and unfolded under the hood. Using TensorFlow is like using WolframAlpha for a calculus problem set.

Another feature of this library is its interactive visualization environment, called TensorBoard. This tool shows a flowchart of the way data transforms, displays summary logs over time, and traces performance. Figure $1.11$ shows what TensorBoard looks like; chapter 2 covers using it.

机器学习代写|tensorflow代写|Overview of future chapters

Chapter 2 demonstrates how to use various components of TensorFlow (see figure 1.12). Chapters $3-10$ show how to implement classic machine-learning algorithms in TensorFlow, and chapters 11-19 cover algorithms based on neural networks. The algorithms solve a wide variety of problems, such as prediction, classification, clustering, dimensionality reduction, and planning.

• TensorFlow has become the tool of choice among professionals and researchers for implementing machine-learning solutions.
• Machine learning uses examples to develop an expert system that can make useful statements about new inputs.A key property of ML is that performance tends to improve with more training data.
• Over the years, scholars have crafted three major archetypes that most problems fit: supervised learning, unsupervised learning, and reinforcement learning. Meta-learning is a new area of ML that focuses on exploring the entire space of models, solutions, and tuning tricks automatically.
• After a real-world problem is formulated in a machine-learning perspective, several algorithms become available. Of the many software libraries and frameworks that can accomplish an implementation, we chose TensorFlow as our silver bullet. Developed by Google and supported by its flourishing community, TensorFlow gives us a way to implement industry-standard code easily.

机器学习代写|tensorflow代写|Computing the inner product

That’s too much code to calculate the inner product of two vectors (also known as the dot product). Imagine how much code would be required for something more complicated, such as solving linear equations or computing the distance between two vectors, if you still lacked TensorFlow and its friends, like the Numerical Python (NumPy) library.

When installing the TensorFlow library, you also install a well-known, robust Python library called NumPy, which facilitates mathematical manipulation in Python. Using Python without its libraries (NumPy and TensorFlow) is like using a camera without an autofocus mode; sure, you gain more flexibility, but you can easily make careless mistakes. (For the record, we have nothing against photographers who micromanage aperture, shutter, and ISO-the so-called “manual” knobs used to prepare your camera to take an image.) It’s easy to make mistakes in machine learning, so let’s keep our camera on autofocus and use TensorFlow to help automate tedious software development.
The following code snippet shows how to write the same inner product concisely using NumPy:
import numpy as np
revenue = np. dot (prices, amounts)
Python is a succinct language. Fortunately for you, this book doesn’t have pages and pages of cryptic code. On the other hand, the brevity of the Python language also implies that a lot is happening behind each line of code, which you should study carefully as you follow along in this chapter. You will find that this is a core theme for TensorFlow, something that it balances elegantly as an add-on library to Python. TensorFlow hides enough of the complexity (like autofocus) but also allows you to turn those magical configurable knobs when you want to get your hands dirty.

Machine-learning algorithms require many mathematical operations. Often, an algorithm boils down to a composition of simple functions iterated until convergence. Sure, you may use any standard programming language to perform these computations, but the secret to both manageable and high-performing code is the use of a well-written library, such as TensorFlow (which officially supports Python, C++, JavaScript, Go, and Swift).

机器学习代写|tensorflow代写|TensorFlow

TensorFlow 最奇特的特性之一是其自动微分功能。您可以试验新网络，而无需重新定义许多关键计算。

机器学习代写|tensorflow代写|Overview of future chapters

• TensorFlow 已成为专业人士和研究人员实施机器学习解决方案的首选工具。
• 机器学习使用示例来开发一个专家系统，该系统可以对新输入做出有用的陈述。ML 的一个关键特性是，随着训练数据的增加，性能往往会提高。
• 多年来，学者们精心设计了适合大多数问题的三种主要原型：监督学习、无监督学习和强化学习。元学习是 ML 的一个新领域，专注于自动探索模型、解决方案和调整技巧的整个空间。
• 从机器学习的角度阐述现实世界的问题后，可以使用几种算法。在可以完成实现的众多软件库和框架中，我们选择了 TensorFlow 作为我们的灵丹妙药。TensorFlow 由 Google 开发并得到其蓬勃发展的社区的支持，为我们提供了一种轻松实现行业标准代码的方法。

机器学习代写|tensorflow代写|Computing the inner product

import numpy as np

Python 是一门简洁的语言。对你来说幸运的是，这本书没有一页又一页的神秘代码。另一方面，Python 语言的简洁性也意味着每一行代码背后都发生了很多事情，在本章中你应该仔细研究这些内容。你会发现这是 TensorFlow 的核心主题，它作为 Python 的附加库优雅地平衡了一些东西。TensorFlow 隐藏了足够多的复杂性（如自动对焦），但还允许您在想弄脏双手时转动那些神奇的可配置旋钮。

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。