深度学习 影印版PDF电子书下载

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 Data Vec 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 379

Common Issues with 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