深度学习框架TensorFlow实战

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"TensorFlow for Deep Learning" 是一本由 Bharath Ramsundar 和 Reza Bosagh Zadeh 合著的书籍，旨在帮助读者深入了解并掌握使用 TensorFlow 进行深度学习的技术。本书不仅涵盖了从线性回归到强化学习的广泛概念，还探讨了数据分析、机器学习和数据库管理等领域的高级主题。在本书中，读者将学习到以下关键知识点： 1. 数据分析技术：包括分类、聚类、回归和预测等方法，这些都是数据分析项目中的基础工具。通过Python代码示例，读者可以了解如何实际应用这些技术。 2. 数据处理：处理结构化和非结构化数据是现代数据分析的重要部分。书中讨论了ETL（提取、转换、加载）技术，用于从各种数据源中获取和整理信息，如网络爬虫在高频算法交易和目标导向对话系统中的应用。 3. 数据库管理：介绍了一些先进的数据库系统，如图数据库Neo4j、搜索引擎Elasticsearch、文档型数据库MongoDB以及关系型数据库MySQL。这些数据库在处理大数据时各有优势，读者将学习如何根据项目需求选择合适的数据库。 4. 大数据框架：书中涵盖Hadoop和Spark等大数据处理框架，这些框架对于处理大规模数据集至关重要。 5. 机器学习概念：深入探讨了半监督学习、深度学习和自然语言处理（NLP）等前沿机器学习技术。深度学习部分将详细介绍TensorFlow这一强大的深度学习库，包括神经网络架构、模型训练和优化等。 6. 应用场景：通过实例展示了如何在实际项目中应用所学知识，例如在强化学习中使用TensorFlow进行智能决策。本书适合对数据科学和分析感兴趣的读者，特别是数据科学家和软件开发者。通过阅读，读者将能够全面了解数据分析项目的各个技术环节，并能将所学应用于自己的项目实践中。注意，此书的ISBN号为978-1-491-98045-3，由O'Reilly Media, Inc.出版，并有在线版本可供访问（http://oreilly.com/safari）。

can be regularly retrained as new data comes in. Very sophisticated software systems,

without major changes to their code (just to their training data). This trend is only

likely to accelerate as machine learning tools and deployment become easier and

easier.

As the behavior of software-engineered systems changes, the roles of software engi‐

neers will change as well. In some ways, this transformation will be analogous to the

transformation following the development of programming languages. The first com‐

puters were painstakingly programmed. Networks of wires were connected and inter‐

connected. Then punchcards were set up to enable the creation of new programs

without hardware changes to computers. Following the punchcard era, the first

assembly languages were created. Then higher-level languages like Fortran or Lisp.

Succeeding layers of development have created very high-level languages like Python,

with intricate ecosystems of precoded algorithms. Much modern computer science

even relies on autogenerated code. Modern app developers use tools like Android

Studio to autogenerate much of the code they’d like to make. Each successive wave of

simplification has broadened the scope of computer science by lowering barriers to

entry.

Machine learning promises to lower barriers even further; programmers will soon be

able to change the behavior of systems by altering training data, possibly without

writing a single line of code. On the user side, systems built on spoken language and

natural language understanding such as Alexa and Siri will allow nonprogrammers to

perform complex computations. Furthermore, ML powered systems are likely to

become more robust against errors. The capacity to retrain models will mean that

codebases can shrink and that maintainability will increase. In short, machine learn‐

ing is likely to completely upend the role of software engineers. Today’s programmers

will need to understand how machine learning systems learn, and will need to under‐

stand the classes of errors that arise in common machine learning systems. Further‐

more, they will need to understand the design patterns that underlie machine

learning systems (very different in style and form from classical software design pat‐

terns). And, they will need to know enough tensor calculus to understand why a

sophisticated deep architecture may be misbehaving during learning. It’s not an

understatement to say that understanding machine learning (theory and practice)

will become a fundamental skill that every computer scientist and software engineer

will need to understand for the coming decade.

In the remainder of this chapter, we will provide a whirlwind tour of the basics of

modern deep learning. The remainder of this book will go into much greater depth

on all the topics we touch on here.

2 | Chapter 1: Introduction to Deep Learning

Deep Learning Architectures

There have been hundreds of different deep learning models that combine the deep

learning primitives presented in the previous section. Some of these architectures

have been historically important. Others were the first presentations of novel designs

that influenced perceptions of what deep learning could do.

In this section, we present a selection of different deep learning architectures that

have proven influential for the research community. We want to emphasize that this

is an episodic history that makes no attempt to be exhaustive. There are certainly

important models in the literature that have not been presented here.

LeNet

The LeNet architecture is arguably the first prominent “deep” convolutional architec‐

ture. Introduced in 1988, it was used to perform optical character recoginition (OCR)

for documents. Although it performed its task admirably, the computational cost of

the LeNet was extreme for the computer hardware available at the time, so the design

languished in (relative) obscurity for a few decades after its creation. This architec‐

ture is illustrated in Figure 1-5.

Figure 1-5. e LeNet architecture for image processing. Introduced in 1988, it was argu‐

ably the rst deep convolutional model for image processing.

AlexNet

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) was first organ‐

ized in 2010 as a test of the progress made in visual recognition systems. The organiz‐

ers made use of Amazon Mechanical Turk, an online platform to connect workers to

requesters, to catalog a large collection of images with associated lists of objects

present in the image. The use of Mechanical Turk permitted the curation of a collec‐

tion of data significantly larger than those gathered previously.

The first two years the challenge ran, more traditional machine-learned systems that

relied on systems like HOG and SIFT features (hand-tuned visual feature extraction

methods) triumphed. In 2012, the AlexNet architecture, based on a modification of

LeNet run on powerful graphics processing units (GPUs), entered and dominated the

6 | Chapter 1: Introduction to Deep Learning

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