Deep Learning with R

J.J. Allaire, François Chollet

• Editore: Manning
• Anno: 2018
• ISBN:
  9781638351634
• Argomento: Computer Science
• Categoria: Neural Networks

Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-?learning-with-r-in-motion).Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. About the Book Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models. What's Inside

• Deep learning from first principles
• Setting up your own deep-learning environment
• Image classification and generation
• Deep learning for text and sequences

About the Reader You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the Authors François Chollet is a deep-learning researcher at Google and the author of the Keras library. J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras. Table of Contents

PART 1 - FUNDAMENTALS OF DEEP LEARNING

• What is deep learning?
• Before we begin: the mathematical building blocks of neural networks
• Getting started with neural networks
• Fundamentals of machine learning

PART 2 - DEEP LEARNING IN PRACTICE

• Deep learning for computer vision
• Deep learning for text and sequences
• Advanced deep-learning best practices
• Generative deep learning
• Conclusions

Part 1. Fundamentals of deep learning

Chapters 1–4 of this book will give you a foundational understanding of what deep learning is, what it can achieve, and how it works. These chapters will also make you familiar with the canonical workflow for solving data problems using deep learning. If you aren’t already highly knowledgeable about deep learning, you should definitely begin by reading part 1 in full before moving on to the practical applications in part 2.

Chapter 1. What is deep learning?

This chapter covers

• High-level definitions of fundamental concepts
• Timeline of the development of machine learning
• Key factors behind deep learning’s rising popularity and future potential

In the past few years, artificial intelligence (AI) has been a subject of intense media hype. Machine learning, deep learning, and AI come up in countless articles, often outside of technology-minded publications. We’re promised a future of intelligent chatbots, self-driving cars, and virtual assistants—a future sometimes painted in a grim light and other times as utopian, where human jobs will be scarce, and most economic activity will be handled by robots or AI agents. For a future or current practitioner of machine learning, it’s important to be able to recognize the signal in the noise so that you can tell world-changing developments from overhyped press releases. Our future is at stake, and it’s a future in which you have an active role to play: after reading this book, you’ll be one of those who develop the AI agents. So let’s tackle these questions: What has deep learning achieved so far? How significant is it? Where are we headed next? Should you believe the hype? This chapter provides essential context around artificial intelligence, machine learning, and deep learning.

1.1. Artificial intelligence, machine learning, and deep learning

First, we need to define clearly what we’re talking about when we mention AI. What are artificial intelligence, machine learning, and deep learning (see figure 1.1)? How do they relate to each other?

Figure 1.1. Artificial intelligence, machine learning, and deep learning

1.1.1. Artificial intelligence

Artificial intelligence was born in the 1950s, when a handful of pioneers from the nascent field of computer science started asking whether computers could be made to “think”—a question whose ramifications we’re still exploring today. A concise definition of the field would be as follows: the effort to automate intellectual tasks normally performed by humans. As such, AI is a general field that encompasses machine learning and deep learning, but that also includes many more approaches that don’t involve any learning. Early chess programs, for instance, only involved hardcoded rules crafted by programmers, and didn’t qualify as machine learning. For a fairly long time, many experts believed that human-level artificial intelligence could be achieved by having programmers handcraft a sufficiently large set of explicit rules for manipulating knowledge. This approach is known as symbolic AI, and it was the dominant paradigm in AI from the 1950s to the late 1980s. It reached its peak popularity during the expert systems boom of the 1980s.

Although symbolic AI proved suitable to solve well-defined, logical problems, such as playing chess, it turned out to be intractable to figure out explicit rules for solving more complex, fuzzy problems, such as image classification, speech recognition, and language translation. A new approach arose to take symbolic AI’s place: machine learning.

1.1.2. Machine learning

In Victorian England, Lady Ada Lovelace was a friend and collaborator of Charles Babbage, the inventor of the Analytical Engine: the first-known general-purpose, mechanical computer. Although visionary and far ahead of its time, the Analytical Engine wasn’t meant as a general-purpose computer when it was designed in the 1830s and 1840s, because the concept of general-purpose computation was yet to be invented. It was merely meant as a way to use mechanical operations to automate certain computations from the field of mathematical analysis—hence, the name Analytical Engine. In 1843, Ada Lovelace remarked on the invention, “The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform.... Its province is to assist us in making available what we’re already acquainted with.”

This remark was later quoted by AI pioneer Alan Turing as “Lady Lovelace’s objection” in his landmark 1950 paper “Computing Machinery and Intelligence,”^[1] which introduced the Turing test as well as key concepts that would come to shape AI. Turing was quoting Ada Lovelace while pondering whether general-purpose computers could be capable of learning and originality, and he came to the conclusion that they could.

¹

A. M. Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (1950): 433-460.

Machine learning arises from this question: could a computer go beyond “whatever we know how to order it to perform” and learn on its own how to perform a specified task? Could a computer surprise us? Rather than programmers crafting data-processing rules by hand, could a computer automatically learn these rules by looking at data?

This question opens the door to a new programming paradigm. In classical programming, the paradigm of symbolic AI, humans input rules (a program) and data to be processed according to these rules, and out come answers (see figure 1.2). With machine learning, humans input data as well as the answers expected from the data, and out come the rules. These rules can then be applied to new data to produce original answers.

Figure 1.2. Machine learning: a new programming paradigm

A machine-learning system is trained rather than explicitly programmed. It’s presented with many examples relevant to a task, and it finds statistical structure in these examples that eventually allows the system to come up with rules for automating the task. For instance, if you wished to automate the task of tagging your vacation pictures, you could present a machine-learning system with many examples of pictures already tagged by humans, and the system would learn statistical rules for associating specific pictures to specific tags.

Although machine learning only started to flourish in the 1990s, it has quickly become the most popular and most successful subfield of AI, a trend driven by the availability of faster hardware and larger datasets. Machine learning is tightly related to mathematical statistics, but it differs from statistics in several important ways. Unlike statistics, machine learning tends to deal with large, complex datasets (such as a dataset of millions of images, each consisting of tens of thousands of pixels) for which classical statistical analysis such as Bayesian analysis would be impractical. As a result, machine learning, and especially deep learning, exhibits comparatively little mathematical theory—maybe too little—and is engineering oriented. It’s a hands-on discipline in which ideas are proven empirically more often than theoretically.

1.1.3. Learning representations from data

To define deep learning and understand the difference between deep learning and other machine-learning approaches, first we need some idea of what machine-learning algorithms do. We just stated that machine learning discovers rules to execute a data-processing task, given examples of what’s expected. So, to do machine learning, we need three things:

• Input data points—For instance, if the task is speech recognition, these data points could be sound files of people speaking. If the task is image tagging, they could be pictures.
• Examples of the expected output—In a speech-recognition task, these could be human-generated transcripts of sound files. In an image task, expected outputs could be tags such as “dog,” “cat,” and so on.
• A way to measure whether the algorithm is doing a good job—This is necessary in order to determine the distance between the algorithm’s current output and its expected output. The measurement is used as a feedback signal to adjust the way the algorithm works. This adjustment step is what we call learning.

A machine...