《TensorFlow深度学习指南》：从线性回归到强化学习

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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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《TensorFlow深度学习指南》：从线性回归到强化学习

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