使用R语言深度学习实战指南

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"《Introduction to Deep Learning Using R》是由Taweh Beysolow II编写的，这是一本关于使用R语言学习和实现深度学习模型的分步指南。书中假设读者已经具备基本的统计学或计算机编程知识，特别是对R语言的理解。对于没有R语言背景的读者，可能会在理解代码示例部分遇到困难，建议先学习R语言基础知识后再阅读本书。" 深度学习是人工智能（AI）领域的一个重要分支，它通过模拟人脑神经网络的方式进行复杂模式识别和决策。这本书《Introduction to Deep Learning Using R》旨在引导读者逐步掌握深度学习的概念和应用，并利用R语言实施这些模型。 R语言是一种广泛用于统计分析和图形可视化的编程语言，它拥有丰富的数据处理和机器学习库，如Keras、tensorflow和h2o等，使得在R中构建深度学习模型变得可能。书中的内容可能涵盖了以下几个方面： 1. **深度学习基础**：介绍深度学习的基本概念，包括神经网络的结构、反向传播算法、梯度下降以及损失函数等。 2. **R语言环境设置**：指导读者如何在R环境中安装必要的深度学习库，例如安装Keras库，该库允许R与TensorFlow等后端框架交互。 3. **数据预处理**：讲解如何清洗和准备数据，包括数据加载、标准化、归一化以及缺失值处理等步骤，这是深度学习模型训练前的关键步骤。 4. **构建模型**：详细介绍如何使用R构建简单的神经网络，如多层感知器（MLP），以及更复杂的模型如卷积神经网络（CNN）和循环神经网络（RNN）。 5. **模型训练与优化**：讨论训练过程中的超参数调整、正则化技术以及训练集与验证集的划分，以防止过拟合和提高模型泛化能力。 6. **模型评估**：介绍评估指标，如准确率、精确率、召回率和F1分数，以及混淆矩阵，帮助读者理解模型性能。 7. **深度学习应用**：可能包括图像分类、自然语言处理、推荐系统等领域的案例研究，展示如何在实际问题中应用深度学习。 8. **实战项目**：提供实际的编程练习，让读者动手构建并训练深度学习模型，以巩固理论知识。 9. **未来发展方向**：探讨深度学习的最新进展，如生成对抗网络（GANs）、强化学习和注意力机制等。通过这本书的学习，读者将能够理解和运用R语言来实现深度学习模型，从而解决实际问题。不过，由于R语言在深度学习领域相对较新，相比于Python等其他语言，其生态系统可能不那么完善，但R的统计背景和强大的数据处理能力使其在特定场景下仍然具有优势。因此，对R语言感兴趣的读者可以通过这本书深入理解深度学习，并结合自身背景，发掘其在数据分析和预测任务中的潜力。

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T. Beysolow II, Introduction to Deep Learning Using R, DOI 10.1007/978-1-4842-2734-3_1

CHAPTER 1

Introduction to Deep

Learning

With advances in hardware and the emergence of big data, more advanced computing

methods have become increasingly popular. Increasing consumer demand for better

products and companies seeking to leverage their resources more efficiently have

also been leading this push. In response to these market forces, we have recently seen

a renewed and widely spoken about interest in the field of machine learning. At the

cross-section of statistics, mathematics, and computer science, machine learning refers

to the science of creating and studying algorithms that improve their own behavior in

an iterative manner by design. Originally, the field was devoted to developing artificial

intelligence, but due to the limitations of the theory and technology that were present

at the time, it became more logical to focus these algorithms on specific tasks. Most

machine learning algorithms as they exist now focus on function optimization, and the

solutions yielded don’t always explain the underlying trends within the data nor give

the inferential power that artificial intelligence was trying to get close to. As such, using

machine learning algorithms often becomes a repetitive trial and error process, in which

the choice of algorithm across problems yields different performance results. This is fine

in some contexts, but in the case of language modeling and computer vision, it becomes

problematic.

In response to some of the shortcomings of machine learning, and the significant

advance in the theoretical and technological capabilities at our disposal today, deep

learning has emerged and is rapidly expanding as one of the most exciting fields of

science. It is being used in technologies such as self-driving cars, image recognition on

social media platforms, and translation of text from one language to others. Deep learning

is the subfield of machine learning that is devoted to building algorithms that explain

and learn a high and low level of abstractions of data that traditional machine learning

algorithms often cannot. The models in deep learning are often inspired by many sources

of knowledge, such as game theory and neuroscience, and many of the models often

mimic the basic structure of a human nervous system. As the field advances, many

researchers envision a world where software isn’t nearly as hard coded as it often needs to

be today, allowing for a more robust, generalized solution to solving problems.

Chapter 1 ■ IntroduCtIon to deep LearnIng

Although it originally started in a space similar to machine learning, where the

primary focus was constraint satisfaction to varying degrees of complexity, deep

learning has now evolved to encompass a broader definition of algorithms that are able

to understand multiple levels of representation of data that correspond to different

hierarchies of complexity. In other words, the algorithms not only have predictive and

classification ability, but they are able to learn different levels of complexity. An example

of this is found in image recognition, where a neural network builds upon recognizing

eyelashes, to faces, to people, and so on. The power in this is obvious: we can reach a level

of complexity necessary to create intelligent software. We see this currently in features

such as autocorrect, which models the suggested corrections to patterns of speech

observed, specific to each person’s vocabulary.

The structure of deep learning models often is such that they have layers of non-linear

units that process data, or neurons, and the multiple layers in these models process different

levels of abstraction of the data. Figure1-1 shows a visualization of the layers of neural

networks.

Figure 1-1. Deep neural network

Deep neural networks are distinguished by having many hidden layers, which

are called “hidden” because we don’t necessarily see what the inputs and outputs of

these neurons are explicitly beyond knowing they are the output of the preceding layer.

The addition of layers, and the functions inside the neurons of these layers, are what

distinguish an individual architecture from another and establish the different use cases

of a given model.

More specifically, lower levels of these models explain the “how,” and the higher-levels

of neural networks process the “why.” The functions used in these layers are dependent

on the use case, but often are customizable by the user, making them significantly more

robust than the average machine learning models that are often used for classification and

regression, for example. The assumption in deep learning models on a fundamental level is

that the data being interpreted is generated by the interactions of different factors organized

Chapter 1 ■ IntroduCtIon to deep LearnIng

in layers. As such, having multiple layers allows the model to process the data such that it

builds an understanding from simple aspects to larger constructs. The objective of these

models is to perform tasks without the same degree of explicit instruction that many

machine learning algorithms need. With respect to how these models are used, one of the

main benefits is the promise they show when applied to unsupervised learning problems,

or problems where we don’t know prior to performing the experiment that the response

variable y should be given a set of explanatory variables x. An example would be image

recognition, particularly after a model has been trained against a given set of data. Let’s say

we input an image of a dog in the testing phase, implying that we don’t tell the model what

the picture is of. The neural network will start by recognizing eyelashes prior to a snout,

prior to the shape of the dog’s head, and so on until it classifies the image as that of a dog.

Deep Learning Models

Now that we have established a brief overview of deep learning, it will be useful to discuss

what exactly you will be learning in this book, as well as describe the models we will be

addressing here.

This text assumes you are relatively informed by an understanding of mathematics

and statistics. Be that as it may, we will briefly review all the concepts necessary to

understand linear algebra, optimization, and machine learning such that we will form

a solid base of knowledge necessary for grasping deep learning. Though it does help to

understand all this technical information precisely, those who don’t feel comfortable with

more advanced mathematics need not worry. This text is written in such a way that the

reader is given all the background information necessary to research it further, if desired.

However, the primary goal of this text is to show readers how to apply machine learning

and deep learning models, not to give a verbose academic treatise on all the theoretical

concepts discussed.

After we have sufficiently reviewed all the prerequisite mathematical and machine

learning concepts, we will progress into discussing machine learning models in detail.

This section describes and illustrates deep learning models.

Single Layer Perceptron Model (SLP)

The single layer perceptron (SLP) model is the simplest form of neural network and

the basis for the more advanced models that have been developed in deep learning.

Typically, we use SLP in classification problems where we need to give the data

observations labels (binary or multinomial) based on inputs. The values in the input

layer are directly sent to the output layer after they are multiplied by weights and a bias

is added to the cumulative sum. This cumulative sum is then put into an activation

function, which is simply a function that defines the output. When that output is above

or below a user-determined threshold, the final output is determined. Researchers

McCulloch-Pitts Neurons described a similar model in the 1940s (see Figure1-2).

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