深度学习框架与AI数据科学

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"AI for Data Science 是一本探讨人工智能在数据科学中应用的书籍，涵盖了深度学习框架、超越深度学习的AI方法、以及如何使用MXNet和TensorFlow构建深度学习网络等主题。" 本书深入剖析了AI如何赋能数据科学，旨在帮助读者理解和掌握人工智能技术在实际数据分析中的应用。首先，书中介绍了关于AI的基础知识，包括AI的定义、工作原理以及它如何促进数据科学的发展。AI通过自动化复杂的数据处理和模式识别，显著提高了数据科学家的工作效率和洞察力。第一章重点讨论了深度学习框架，如深度学习系统的概念、运行机制以及主要的深度学习框架，如TensorFlow、PyTorch、Keras和MXNet等。此外，还涉及了深度学习所用的主要编程语言，如Python，并阐述了如何有效利用这些框架来开发和优化模型。同时，本章还概述了深度学习的各种方法和应用场景，例如图像识别、自然语言处理和推荐系统等。第二章扩展了AI的视野，不仅限于深度学习，还涉及了优化算法、模糊推理系统和人工创造力等其他AI方法。这部分内容揭示了AI在解决非结构化问题和复杂决策时的潜力，为数据科学家提供了更多解决问题的工具。此外，书中还对未来的AI发展趋势进行了展望。第三章和第四章分别详细讲解了使用MXNet和TensorFlow构建深度学习网络的实践步骤。MXNet章节介绍了其核心组件，展示了如何在实际项目中运用MXNet，并提供了一些实用技巧。TensorFlow章节则详细解析了TensorFlow的架构，核心组件，以及如何在TensorFlow中进行可视化（通过TensorBoard）。同时，还介绍了高阶API Estimators，使得模型构建更加高效。这本书全面地涵盖了AI在数据科学领域的基础知识和实践经验，对于想要提升AI技能的数据科学家或者希望将AI技术应用于业务解决方案的从业者来说，是一本极具价值的参考书。通过深入阅读和实践，读者可以掌握构建和应用AI模型的关键技能，进一步推动数据驱动的创新和决策。

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About AI

But what exactly is AI? For starters, it’s nothing like what sci-fi

books and films make it out to be. Modern AI technology helps

to facilitate various processes in a more automatic and

autonomous way, with little to no supervision from a human

user. AI initiatives are realistic and purely functional. Although

we can dream about what AI may evolve into someday, as AI

practitioners, we focus on what we know and what we are

certain about, rather than what could exist in a few decades.

AI comprises a set of algorithms that make use of information –

mainly in the form of data – to make decisions and carry out

tasks, much like a human would. Of course, the emulation of

human intelligence is not an easy task; as such, the AIs of today

are rudimentary and specialized. Despite their shortcomings,

though, these modern systems can be particularly good at the

tasks they undertake, even better than humans. For example, an

AI system, which is a standalone program implementing one or

more AI algorithms, that is created for identifying words from

speech, can be more accurate than humans doing the same task.

It’s important to note that all the AI systems we have today

possess what is termed narrow artificial intelligence. This means

that current AIs can do a limited set of tasks (or even just a

single task) quite well, but offer at best mediocre performance at

any other task. For instance, an AI might be great at figuring out

your age based on a headshot, but that same AI almost certainly

couldn’t tell a classical music piece from a pop song.

Some AIs are designed to be used in robots, such as those

designed for rescue missions, able to navigate various terrains.

Other AIs are specialized in crunching data and facilitating

various data analytics tasks. There are even AIs that emulate

creative processes, like the AIs that generate artistic works,

using the patterns they deduce from catalogs of existing work.

Chatbots and other such AIs are focused solely on interacting

with humans. The possibility of a more generalist AI (called

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working in data science for years without these fancy AI

algorithms. While it is certainly possible to gain valuable

insights using traditional data science, AI-based algorithms can

often bring about better performance in our models – the

mathematical abstractions we create to simulate the phenomena

we study. In highly competitive industries, this extra

performance gained from AI-based data models can offer an edge

over others in the market. Because many companies in these

industries already have abundant data they can use to train the

AI algorithms, we term them AI-ready.

AI is now far easier to apply than ever before. The early

developers of AI have proven that AI can deliver a lot of value

to the world, without the help of a rocket scientist to make it

work. This is largely thanks to a series of powerful AI frameworks

and libraries that make AI methods more accessible to most data

science practitioners.

In addition, AI has now diversified and matured enough to

outperform conventional data science methods in many

applications. For this, we must thank the increased computing

resources at our disposal, particularly computing power. This is

something made possible due to the graphics processing units

(GPUs) becoming cheaper and easier to integrate to a computer,

as add-ons. What’s more, cloud computing has become more

mainstream, enabling more people to have access to a virtual

computer cluster, which they customize and rent, to run their AI

projects. This makes AI systems easily scalable and cost-

effective, while at the same time fostering experimentation and

new use cases for this technology.

All this cooperation between AI and data science has led to a lot

of research interest in AI. Research centers, individual

researchers, and the R&D departments of various large

companies have been investigating new ways to make these AI

algorithms more scalable and more robust. This naturally boosts

the field’s impact on the world and makes it a more attractive

technology—not just for the researchers, but for anyone willing

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to tinker with it, including many entrepreneurs in this field.

So yes, data science could continue to happen without AI. But in

many cases, this wouldn’t make much sense. It is now clear that

the world of data science has a lot of problems and limitations

that AI can help address. The overlap of these two closely

related fields will only continue to grow as they both develop, so

now is the perfect time to jump into learning AI with both feet.

About the book

This book covers various frameworks, focusing on the most

promising ones, while also considering different AI algorithms

that go beyond deep learning. Hopefully, this book will give you

a more holistic understanding of the field of AI, arming you

with a wide variety of tools (not just the ones currently in the

limelight). With multiple tools at your disposal, you can make

your own decision about which one is best for any given data-

related problem. After all, a good data scientist must not only

know how to use each and every tool in the toolbox, but which

tool is right for the job at hand.

Although most technologists and executives involved in data-

driven processes can benefit significantly from this book, it is

most suitable for data science professionals, AI practitioners,

and those in related disciplines (such as Python or Julia

programmers).

A basic understanding of data science is an important

prerequisite to this book (for a thorough introduction to this, feel

free to check out the book “Data Science Mindset,

Methodologies, and Misconceptions” by Technics Publications).

Moreover, a good mathematical background is recommended

for those who want to dig deeper into the methods we examine

in this book. Ultimately, though, the most important

qualifications are determination and a curious nature, since you

will ultimately put this knowledge into practice building your

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own AI systems.

Although this book is heavy on programming, you can still

derive some useful understanding of AI, even if you don’t do

any coding. However, for best results, we recommend you work

through the various examples and perhaps experiment a little on

your own. We created a Docker image of all the code and data

used in the book’s examples, so you can follow along and

experiment. See Appendix F for how to set up this environment

and use the corresponding code.

This book provides an easy transition for someone with some

understanding of the more well-known aspects of AI. As such,

we start with an overview of the deep learning frameworks

(chapter 1), followed by a brief description of the other AI

frameworks, focusing on optimization algorithms and fuzzy logic

systems (chapter 2). The objective of these first two chapters is to

provide you with some frame of reference, before proceeding to

the more hands-on and specialized aspects.

Namely, in chapter 3 we examine the MXNet framework for

deep learning and how it works on Python. The focus here is on

the most basic (and most widely used) deep learning systems,

namely feed forward neural networks (also known as multi-layer

perceptrons, or MLPs for short). The two chapters that follow

examine these deep learning systems using other popular

frameworks: Tensorflow and Keras. All of the deep learning

chapters contain some examples (provided as Jupyter notebooks

with Python code in the Docker image) for hands-on practice on

these systems.

Chapters 6 through 8 examine optimization algorithms,

particularly the more advanced ones. Each chapter focuses on a

particular framework of these algorithms, including particle

swarm optimization (PSO), genetic algorithms (GAs), and simulated

annealing (SA). We also consider applications of these

algorithms, and how they can be of use in data science projects.

The programming language we’ll be using for these chapters is

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深度学习框架与AI数据科学

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import pandas as pd

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