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Deep learning : a practitioner's approach

Deep learning : a practitioner's approach (11회 대출)

자료유형
단행본
개인저자
Patterson, Josh. Gibson, Adam.
서명 / 저자사항
Deep learning : a practitioner's approach / Josh Patterson and Adam Gibson.
발행사항
Sebastopol, CA :   O'Reilly,   c2017.  
형태사항
xxi, 507 p. : ill. ; 24 cm.
ISBN
9781491914250
서지주기
Includes bibliographical references and index.
일반주제명
Machine learning. Neural networks (Computer science). Open source software.
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008 170904s2017 caua b 001 0 eng d
020 ▼a 9781491914250
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.31 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b P317d
100 1 ▼a Patterson, Josh.
245 1 0 ▼a Deep learning : ▼b a practitioner's approach / ▼c Josh Patterson and Adam Gibson.
260 ▼a Sebastopol, CA : ▼b O'Reilly, ▼c c2017.
300 ▼a xxi, 507 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Machine learning.
650 0 ▼a Neural networks (Computer science).
650 0 ▼a Open source software.
700 1 ▼a Gibson, Adam.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.31 P317d 등록번호 121241482 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

저자소개

애덤 깁슨(지은이)

샌프란시스코에서 활동하는 딥러닝 전문가로 포춘 500대 기업, 헤지 펀드, 광고회사 및 스타트업 지원 회사와 함께 머신러닝 프로젝트를 수행하고 있다. 회사가 실시간 빅데이터를 처리하고 분석할 수 있게 해주는 분야에서 매우 뛰어난 실적을 냈다. 13살 때부터 컴퓨터를 사랑했으며 http://deeplearning4j.orj를 통해 오픈소스 커뮤니티에 적극적으로 참여한다.

조시 패터슨(지은이)

현재 스카이마인드의 기술 분야 책임자다. 그전에는 빅데이터/머신러닝/딥러닝 분야 컨설팅을 했다. 클라우데라의 수석 솔루션 아키텍트로 근무했고, 테네시 강 유역 개발공사에서 머신러닝/분산 시스템 엔지니어로 일하며 openPDC 프로젝트의 지능형 전력망 시스템(스마트 그리드)에 하둡을 도입했다. 테네시 대학교에서 tinyOS 기반 그물형 네트워크와 사회성곤충 최적화 알고리즘 연구로 컴퓨터 공학 석사학위를 취득했다. 소프트웨어 개발 분야에서 17년 이상 근무했으며 DL4J, 아파치 머하웃, 메트로놈(Metronome), IterativeReduce, openPDC, 제이모티프(JMotif)와 같은 오픈소스 공간에 매우 적극적으로 참여한다.

정보제공 : Aladin

목차

CONTENTS
Preface = xiii
1. A Review of Machine Learning = 1
 The Learning Machines = 1
  How Can Machines Learn? = 2
  Biological Inspiration = 4
  What Is Deep Learning? = 6
  Going Down the Rabbit Hole = 7
 Framing the Questions = 8
  The Math Behind Machine Learning : Linear Algebra = 8
  Scalars = 9
  Vectors = 9
  Matrices = 10
  Tensors = 10
  Hyperplanes = 10
  Relevant Mathematical Operations = 11
  Converting Data Into Vectors = 11
  Solving Systems of Equations = 13
 The Math Behind Machine Learning : Statistics = 15
  Probability = 16
  Conditional Probabilities = 18
  Posterior Probability = 19
  Distributions = 19
  Samples Versus Population = 22
  Resampling Methods = 22
  Selection Bias = 22
  Likelihood = 23
 How Does Machine Learning Work? = 23
  Regression = 23
  Classification = 25
  Clustering = 26
  Underfitting and Overfitting = 26
  Optimization = 27
  Convex Optimization = 29
  Gradient Descent = 30
  Stochastic Gradient Descent = 32
  Quasi-Newton Optimization Methods = 33
  Generative Versus Discriminative Models = 33
 Logistic Regression = 34
  The Logistic Function = 35
  Understanding Logistic Regression Output = 35
 Evaluating Models = 36
  The Confusion Matrix = 36
 Building an Understanding of Machine Learning = 40
2. Foundations of Neural Networks and Deep Learning = 41
 Neural Networks = 41
  The Biological Neuron = 43
  The Perceptron = 45
  Multilayer Feed-Forward Networks = 50
 Training Neural Networks = 56
  Backpropagation Learning = 57
 Activation Functions = 65
  Linear = 66
  Sigmoid = 66
  Tanh = 67
  Hard Tanh = 68
  Softmax = 68
  Rectified Linear = 69
 Loss Functions = 71
  Loss Function Notation = 71
  Loss Functions for Regression = 72
  Loss Functions for Classification = 75
  Loss Functions for Reconstruction = 77
 Hyperparameters = 78
  Learning Rate = 78
  Regularization = 79
  Momentum = 79
  Sparsity = 80
3. Fundamentals of Deep Networks = 81
 Defining Deep Learning = 81
  What Is Deep Learning? = 81
  Organization of This Chapter = 91
 Common Architectural Principles of Deep Networks = 92
  Parameters = 92
  Layers = 93
  Activation Functions = 93
  Loss Functions = 95
  Optimization Algorithms = 96
  Hyperparameters = 100
  Summary = 105
 Building Blocks of Deep Networks = 105
  RBMs = 106
  Autoencoders = 112
  Variational Autoencoders = 114
4. Major Architectures of Deep Networks = 117
 Unsupervised Pretrained Networks = 118
  Deep Belief Networks = 118
  Generative Adversarial Networks = 121
 Convolutional Neural Networks (CNNs) = 125
  Biological Inspiration = 126
  Intuition = 126
  CNN Architecture Overview = 128
  Input Layers = 130
  Convolutional Layers = 130
  Pooling Layers = 140
  Fully Connected Layers = 140
  Other Applications of CNNs = 141
  CNNs of Note = 141
  Summary = 142
 Recurrent Neural Networks = 143
  Modeling the Time Dimension = 143
  3D Volumetric Input = 146
  Why Not Markov Models? = 148
  General Recurrent Neural Network Architecture = 149
  LSTM Networks = 150
  Domain-Specific Applications and Blended Networks = 159
 Recursive Neural Networks = 160
  Network Architecture = 160
  Varieties of Recursive Neural Networks = 161
  Applications of Recursive Neural Networks = 161
 Summary and Discussion = 162
  Will Deep Learning Make Other Algorithms Obsolete? = 162
  Different Problems Have Different Best Methods = 162
  When Do I Need Deep Learning? = 163
5. Building Deep Networks = 165
 Matching Deep Networks to the Right Problem = 165
  Columnar Data and Multilayer Perceptrons = 166
  Images and Convolutional Neural Networks = 166
  Time-series Sequences and Recurrent Neural Networks = 167
  Using Hybrid Networks = 169
 The DL4J Suite of Tools = 169
  Vectorization and DataVec = 170
  Runtimes and ND4J = 170
 Basic Concepts of the DL4J API = 172
  Loading and Saving Models = 172
  Getting Input for the Model = 173
  Setting Up Model Architecture = 173
  Training and Evaluation = 174
 Modeling CSV Data with Multilayer Perceptron Networks = 175
  Setting Up Input Data = 178
  Determining Network Architecture = 178
  Training the Model = 181
  Evaluating the Model = 181
 Modeling Handwritten Images Using CNNs = 182
  Java Code Listing for the LeNet CNN = 183
  Loading and Vectorizing the Input Images = 185
  Network Architecture for LeNet in DL4J = 186
  Training the CNN = 190
 Modeling Sequence Data by Using Recurrent Neural Networks = 191
  Generating Shakespeare via LSTMs = 191
  Classifying Sensor Time-series Sequences Using LSTMs = 200
 Using Autoencoders for Anomaly Detection = 207
  Java Code Listing for Autoencoder Example = 207
  Setting Up Input Data = 211
  Autoencoder Network Architecture and Training = 211
  Evaluating the Model = 213
 Using Variational Autoencoders to Reconstruct MNIST Digits = 214
  Code Listing to Reconstruct MNIST Digits = 214
  Examining the VAE Model = 217
 Applications of Deep Learning in Natural Language Processing = 221
  Learning Word Embedding Using Word2Vec = 221
  Distributed Representations of Sentences with Paragraph Vectors = 227
  Using Paragraph Vectors for Document Classification = 231
6. Tuning Deep Networks = 237
 Basic Concepts in Tuning Deep Networks = 237
  An Intuition for Building Deep Networks = 238
  Building the Intuition as a Step-by-Step Process = 239
 Matching Input Data and Network Architectures = 240
  Summary = 241
 Relating Model Goal and Output Layers = 242
  Regression Model Output Layer = 242
  Classification Model Output Layer = 243
 Working with Layer Count, Parameter Count, and Memory = 246
  Feed-Forward Multilayer Neural Networks = 246
  Controlling Layer and Parameter Counts = 247
  Estimating Network Memory Requirements = 250
 Weight Initialization Strategies = 251
 Using Activation Functions = 253
  Summary Table for Activation Functions = 255
 Applying Loss Functions = 256
 Understanding Learning Rates = 258
  Using the Ratio of Updates-to-Parameters = 259
  Specific Recommendations for Learning Rates = 260
 How Sparsity Affects Learning = 263
 Applying Methods of Optimization = 263
  SGD Best Practices = 265
 Using Parallelization and GPUs for Faster Training = 265
  Online Learning and Parallel Iterative Algorithms = 266
  Parallelizing SGD in DL4J = 269
  GPUs = 272
 Controlling Epochs and Mini-Batch Size = 273
  Understanding Mini-Batch Size Trade-Offs = 274
 How to Use Regularization = 275
  Priors as Regularizers = 275
  Max-Norm Regularization = 276
  Dropout = 277
  Other Regularization Topics = 279
 Working with Class Imbalance = 280
  Methods for Sampling Classes = 282
  Weighted Loss Functions = 282
 Dealing with Overfitting = 283
 Using Network Statistics from the Tuning UI = 284
  Detecting Poor Weight Initialization = 287
  Detecting Nonshuffled Data = 288
  Detecting Issues with Regularization = 290
7. Tuning Specific Deep Network Architectures = 293
 Convolutional Neural Networks (CNNs) = 293
  Common Convolutional Architectural Patterns = 294
  Configuring Convolutional Layers = 297
  Configuring Pooling Layers = 303
  Transfer Learning = 304
 Recurrent Neural Networks = 306
  Network Input Data and Input Layers = 307
  Output Layers and RnnOutputLayer = 308
  Training the Network = 309
  Debugging Common Issues with LSTMs = 311
  Padding and Masking = 312
  Evaluation and Scoring With Masking = 313
  Variants of Recurrent Network Architectures = 314
 Restricted Boltzmann Machines = 314
  Hidden Units and Modeling Available Information = 315
  Using Different Units = 316
  Using Regularization with RBMs = 317
 DBNs = 317
  Using Momentum = 318
  Using Regularization = 319
  Determining Hidden Unit Count = 320
8. Vectorization = 321
 Introduction to Vectorization in Machine Learning = 321
  Why Do We Need to Vectorize Data? = 322
  Strategies for Dealing with Columnar Raw Data Attributes = 325
  Feature Engineering and Normalization Techniques = 327
 Using DataVec for ETL and Vectorization = 334
 Vectorizing Image Data = 336
  Image Data Representation in DL4J = 337
  Image Data and Vector Normalization with DataVec = 339
 Working with Sequential Data in Vectorization = 340
  Major Variations of Sequential Data Sources = 340
  Vectorizing Sequential Data with DataVec = 341
 Working with Text in Vectorization = 347
  Bag of Words = 348
  TF-IDF = 349
  Comparing Word2Vec and VSM Comparison = 353
 Working with Graphs = 354
9. Using Deep Learning and DL4J on Spark = 357
 Introduction to Using DL4J with Spark and Hadoop = 357
  Operating Spark from the Command Line = 360
 Configuring and Tuning Spark Execution = 362
  Running Spark on Mesos = 363
  Running Spark on YARN = 364
  General Spark Tuning Guide = 367
  Tuning DL4J Jobs on Spark = 371
 Setting Up a Maven Project Object Model for Spark and DL4J = 372
  A pom.xml File Dependency Template = 374
  Setting Up a POM File for CDH 5.X = 378
  Setting Up a POM File for HDP 2.4 = 378
 Troubleshooting Spark and Hadoop = 37
  Common Issue swith ND4J = 380
 DL4J Parallel Execution on Spark = 381
  A Minimal Spark Training Example = 383
 DL4J API Best Practices for Spark = 385
 Multilayer Perceptron Spark Example = 387
  Setting Up MLP Network Architecture for Spark = 390
  Distributed Training and Model Evaluation = 390
  Building and Executing a DL4J Spark Job = 392
 Generating Shakespeare Text with Spark and Long Short-Term Memory = 392
  Setting Up the LSTM Network Architecture = 395
  Training, Tracking Progress, and Understanding Results = 396
 Modeling MNIST with a Convolutional Neural Network on Spark = 397
  Configuring the Spark Job and Loading MNIST Data = 400
  Setting Up the LeNet CNN Architecture and Training = 401
A. What Is Artificial Intelligence? = 405
B. RL4J and Reinforcement Learning = 417
C. Numbers Everyone Should Know = 441
D. Neural Networks and Backpropagation : A Mathematical Approach = 443
E. Using the ND4J API = 449
F. Using DataVec = 463
G. Working with DL4J from Source = 475
H. Setting Up DL4J Projects = 477
I. Setting Up GPUs for DL4J Projects = 483
J. Troubleshooting DL4J Installations = 487
Index = 495

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