从零开始：Scikit-Learn与TensorFlow实战机器学习

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"《动手实践机器学习：用Scikit-Learn与TensorFlow》是一本面向初学者的实战指南，作者Aurélien Géron假设读者对机器学习的基础知识了解甚少。该书旨在提供理论概念、直觉和实用工具，帮助读者开发能从数据中学习的程序。书中涵盖了广泛的机器学习技术，从基础的线性回归到深度学习中的前沿算法，如常在竞赛中获胜的方法。书中的教学方法强调实践，通过具体的例子和少量理论来培养对机器学习的直观理解。作者利用成熟的Python框架进行讲解，首先介绍易于上手的Scikit-Learn，它提供了众多高效实现的机器学习算法，是学习者入门的理想选择。接着，作者引入TensorFlow，这是一个复杂的分布式数值计算库，特别适合训练大型神经网络，通过数据流图模型在多GPU服务器上高效运行。TensorFlow由Google创建，并广泛支持大规模机器学习应用，于2015年开源。阅读本书时，虽然不强制使用笔记本进行实践，但强烈建议读者在Aurélien Géron提供的在线Jupyter notebook版本（<https://github.com/ageron/handson-ml>）上尝试代码示例，以获得更深的理解和实践经验。这本书不仅提供概念和技巧，还引导读者构建智能系统，无论你是初学者还是想进一步提升技能的工程师，都是一部值得深入学习的实用资源。版权信息显示，本书于2017年出版，享有Aurélien Géron的著作权，可供教育、商业或促销用途购买，同时提供在线版供选。"

Or maybe your company has tons of data (user logs, financial data, production data,

machine sensor data, hotline stats, HR reports, etc.), and more than likely you could

unearth some hidden gems if you just knew where to look; for example:

•

Segment customers and find the best marketing strategy for each group

• Recommend products for each client based on what similar clients bought

•

Detect which transactions are likely to be fraudulent

• Predict next year’s revenue

• And more

Whatever the reason, you have decided to learn Machine Learning and implement it

in your projects. Great idea!

Objective and Approach

This book assumes that you know close to nothing about Machine Learning. Its goal

is to give you the concepts, the intuitions, and the tools you need to actually imple‐

ment programs capable of learning from data.

We will cover a large number of techniques, from the simplest and most commonly

used (such as linear regression) to some of the Deep Learning techniques that regu‐

larly win competitions.

Rather than implementing our own toy versions of each algorithm, we will be using

actual production-ready Python frameworks:

• Scikit-Learn is very easy to use, yet it implements many Machine Learning algo‐

rithms efficiently, so it makes for a great entry point to learn Machine Learning.

• TensorFlow is a more complex library for distributed numerical computation

using data flow graphs. It makes it possible to train and run very large neural net‐

works efficiently by distributing the computations across potentially thousands

of multi-GPU servers. TensorFlow was created at Google and supports many of

their large-scale Machine Learning applications. It was open-sourced in Novem‐

ber 2015.

The book favors a hands-on approach, growing an intuitive understanding of

Machine Learning through concrete working examples and just a little bit of theory.

While you can read this book without picking up your laptop, we highly recommend

you experiment with the code examples available online as Jupyter notebooks at

https://github.com/ageron/handson-ml.

xiv | Preface

Part II, Neural Networks and Deep Learning, covers the following topics:

• What are neural nets? What are they good for?

•

Building and training neural nets using TensorFlow.

• The most important neural net architectures: feedforward neural nets, convolu‐

tional nets, recurrent nets, long short-term memory (LSTM) nets, and autoen‐

coders.

• Techniques for training deep neural nets.

• Scaling neural networks for huge datasets.

• Reinforcement learning.

The first part is based mostly on Scikit-Learn while the second part uses TensorFlow.

Don’t jump into deep waters too hastily: while Deep Learning is no

doubt one of the most exciting areas in Machine Learning, you

should master the fundamentals first. Moreover, most problems

can be solved quite well using simpler techniques such as Random

Forests and Ensemble methods (discussed in Part I). Deep Learn‐

ing is best suited for complex problems such as image recognition,

speech recognition, or natural language processing, provided you

have enough data, computing power, and patience.

Other Resources

Many resources are available to learn about Machine Learning. Andrew Ng’s ML

course on Coursera and Geoffrey Hinton’s course on neural networks and Deep

Learning are amazing, although they both require a significant time investment

(think months).

There are also many interesting websites about Machine Learning, including of

course Scikit-Learn’s exceptional User Guide. You may also enjoy Dataquest, which

provides very nice interactive tutorials, and ML blogs such as those listed on Quora.

Finally, the Deep Learning website has a good list of resources to learn more.

Of course there are also many other introductory books about Machine Learning, in

particular:

•

Joel Grus, Data Science from Scratch (O’Reilly). This book presents the funda‐

mentals of Machine Learning, and implements some of the main algorithms in

pure Python (from scratch, as the name suggests).

•

Stephen Marsland, Machine Learning: An Algorithmic Perspective (Chapman and

Hall). This book is a great introduction to Machine Learning, covering a wide

xvi | Preface

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