R深度学习基础：无监督建模实战指南

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《R深度学习基础》(RDeepLearningEssentials)是一本由Dr. Joshua F. Wiley所著的专业书籍，专为读者讲解如何利用R语言进行自动分类和预测模型构建，尤其侧重于深度学习技术的应用。该书出版于2016年3月，版权属于Packt Publishing，并受到版权法保护，未经许可不得复制、存储或传输。书中内容旨在提供准确的信息，但销售时并不保证其绝对无误，作者和出版社不对由此产生的任何损失或声称的损失负责。在R深度学习领域，本书覆盖了以下关键知识点： 1. **R语言基础**：作为教学起点，书中会介绍如何在R环境中设置和配置深度学习所需的库和工具，如TensorFlow、Keras或MXNet等R接口，以便用户能够无缝地将这些强大框架融入到R编程环境中。 2. **神经网络基础**：读者可以了解到深度学习的核心概念，包括前馈神经网络（Feedforward Neural Networks）、卷积神经网络（Convolutional Neural Networks, CNNs）和循环神经网络（Recurrent Neural Networks, RNNs），以及它们在图像识别、自然语言处理等领域的应用。 3. **深度学习算法**：书中详细介绍了深度学习算法的实现方法，如反向传播（Backpropagation）、梯度下降（Gradient Descent）以及优化器（如Adam、SGD等），以及如何调整超参数以优化模型性能。 4. **数据预处理与特征工程**：深度学习对数据质量有高要求，因此，本书会涉及如何清洗、标准化和增强数据，以及如何进行特征选择和提取，以提高模型的训练效果。 5. **案例研究与实战项目**：通过实际案例和项目，作者展示了如何在R中应用深度学习解决现实世界的问题，如推荐系统、时间序列分析或文本分类等。这些实践环节有助于读者理解和掌握深度学习的实际应用。 6. **深度学习工具包**：针对不同场景和需求，R深度学习可能涉及到如reticulate、kerasR、tensorflow等特定工具包的使用教程，以及如何有效地整合这些工具进行高效开发。 7. **模型评估与调试**：本书会讲解如何评估模型的性能，如准确率、精确率、召回率和F1分数等指标，以及如何调试模型以避免过拟合或欠拟合等问题。 8. **最新进展与未来发展**：由于深度学习领域发展迅速，书中还会简要介绍当时的最新研究成果和趋势，以帮助读者保持更新，了解深度学习的前沿动态。《R深度学习基础》是一本实用的指南，适合R语言开发者、数据科学家和机器学习爱好者深入理解并掌握R语言中的深度学习技术。通过阅读本书，读者不仅能够学会构建深度学习模型，还能提升在实际问题中的应用能力。

[ 1 ]

Getting Started with

Deep Learning

This chapter discusses deep learning, a powerful multi-layered architecture for

pattern recognition, signal detection, and classication or prediction. Although deep

learning is not new, it is only in the past decade that it has gained great popularity,

due in part to advances in computational capacity and new ways of more efciently

training models, as well as the availability of ever-increasing amounts of data. In this

chapter, you will learn what deep learning is, the R packages available for training

such models, how to get your system set up for analysis, and how to connect R with

H2O, which we will use for many of the examples and work in later chapters on how

to actually train and use a deep learning model.

In this chapter, we will explore the following topics:

• What is deep learning?

• R packages that train deep learning models such as deep belief networks or

deep neural networks

• Connecting R and H2O, the main package we will be using for deep learning

Getting Started with Deep Learning

[ 2 ]

What is deep learning?

To understand what deep learning is, perhaps it is easiest to start with what is

meant by regular machine learning. In general terms, machine learning is devoted

to developing and using algorithms that learn from raw data in order to make

predictions. Prediction is a very general term. For example, predictions from

machine learning may include predicting how much money a customer will spend

at a given company, or whether a particular credit card purchase is fraudulent.

Predictions also encompass more general pattern recognition, such as what letters

are present in a given image, or whether a picture is of a horse, dog, person, face,

building, and so on. Deep learning is a branch of machine learning where a multi-

layered (deep) architecture is used to map the relations between inputs or observed

features and the outcome. This deep architecture makes deep learning particularly

suitable for handling a large number of variables and allows deep learning to

generate features as part of the overall learning algorithm, rather than feature

creation being a separate step. Deep learning has proven particularly effective in

the elds of image recognition (including handwriting as well as photo or object

classication) and natural language processing, such as recognizing speech.

There are many types of machine learning algorithms. In this book, we are primarily

going to focus on neural networks as these have been particularly popular in deep

learning. However, this focus does not mean that it is the only technique available in

machine learning or even deep learning, nor that other techniques are not valuable

or even better suited, depending on the specic task. The next sections will discuss

what neural networks and deep neural networks are conceptually in more depth.

Conceptual overview of neural networks

As their name suggests, neural networks draw their inspiration from neural

processes and neurons in the body. Neural networks contain a series of neurons,

or nodes, which are interconnected and process input. The connections between

neurons are weighted, with these weights based on the function being used and

learned from the data. Activation in one set of neurons and the weights (adaptively

learned from the data) may then feed into other neurons, and the activation of some

nal neuron(s) is the prediction.

To make this process more concrete, an example from human visual perception may

be helpful. The term grandmother cell is used to refer to the concept that somewhere

in the brain there is a cell or neuron that responds specically to a complex and

specic object, such as your grandmother. Such specicity would require thousands

of cells to represent every unique entity or object we encounter. Instead, it is

thought that visual perception occurs by building up more basic pieces into complex

representations. For example, the following is a picture of a square:

Chapter 1

[ 5 ]

Neural networks are used to refer to a broad class of models and algorithms. Hidden

neurons are generated based on some combination of the observed data, similar to

a basis expansion in other statistical techniques; however, rather than choosing the

form of the expansion, the weights used to create the hidden neurons are learned

from the data. Neural networks can involve a variety of activation function(s), which

are transformations of the weighted raw data inputs to create the hidden neurons.

A common choice for activation functions is the sigmoid function:

( )

−

and

the hyperbolic tangent function

( ) ( )

tanhf x x=

. Finally, radial basis functions are

sometimes used as they are efcient function approximators. Although there are a

variety of these, the Gaussian form is common:

( )

exp

f x

 

−

 

= −

 

 

In a shallow neural network such as is shown in Figure 1.3, with only a single hidden

layer, from the hidden units to the outputs is essentially a standard regression

or classication problem. The hidden units can be denoted by h, the outputs by

Y. Different outputs can be denoted by subscripts i = 1, …, k and may represent

different possible classications, such as (in our case) a circle or square. The paths

from each hidden unit to each output are the weights and for the i

output are

denoted by w

. These weights are also learned from the data, just like the weights

used to create the hidden layer. For classication, it is common to use a nal

transformation, the

softmax function, which is

k w

∑

as this ensures that

the estimates are positive (using the exponential function) and that the probability of

being in any given class sums to one. For linear regression, the identity function,

which returns its input, is commonly used. Confusion may arise as to why there

are paths between every hidden unit and output as well as every input and hidden

unit. These are commonly drawn to represent that a priori any of these relations are

allowed to exist. The weights must then be learned from the data, with zero or near

zero weights essentially equating to dropping unnecessary relations.

This only scratches the surface of the conceptual and practical aspects of neural

networks. For a slightly more in-depth introduction to neural networks, see

Chapter 11 of Hastie, T., Tibshirani, R., and Friedman, J. (2009), which is freely

available at

http://statweb.stanford.edu/~tibs/ElemStatLearn/, Chapter 16

of Murphy, K. P. (2012), and Chapter 5 of Bishop, C. M. (2006). Next, we will turn to a

brief introduction to deep neural networks.

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