Deep Learning. A Practitioner's Approach Josh Patterson, Adam Gibson

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Spis treści książki

• Preface
  • Whats in This Book?
  • Who Is The Practitioner?
  • Who Should Read This Book?
    • The Enterprise Machine Learning Practitioner
      • The practicing data scientist
      • The Java engineer
    • The Enterprise Executive
    • The Academic
  • Conventions Used in This Book
  • Using Code Examples
  • Administrative Notes
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
    • Josh
    • Adam
• 1. A Review of Machine Learning
  • The Learning Machines
    • How Can Machines Learn?
    • Biological Inspiration
    • What Is Deep Learning?
    • Going Down the Rabbit Hole
  • Framing the Questions
  • The Math Behind Machine Learning: Linear Algebra
    • Scalars
    • Vectors
    • Matrices
    • Tensors
    • Hyperplanes
    • Relevant Mathematical Operations
      • Dot product
      • Element-wise product
      • Outer product
    • Converting Data Into Vectors
    • Solving Systems of Equations
      • Methods for solving systems of linear equations
      • Iterative methods
      • Iterative methods and linear algebra
  • The Math Behind Machine Learning: Statistics
    • Probability
    • Conditional Probabilities
    • Posterior Probability
    • Distributions
    • Samples Versus Population
    • Resampling Methods
    • Selection Bias
    • Likelihood
  • How Does Machine Learning Work?
    • Regression
      • Setting up the model
      • Visualizing linear regression
      • Relating the linear regression model
    • Classification
    • Clustering
    • Underfitting and Overfitting
    • Optimization
    • Convex Optimization
    • Gradient Descent
    • Stochastic Gradient Descent
      • Mini-batch training and SGD
    • Quasi-Newton Optimization Methods
    • Generative Versus Discriminative Models
  • Logistic Regression
    • The Logistic Function
    • Understanding Logistic Regression Output
  • Evaluating Models
    • The Confusion Matrix
      • Sensitivity versus specificity
      • Accuracy
      • Precision
      • Recall
      • F1
      • Context and interpreting scores
  • Building an Understanding of Machine Learning
• 2. Foundations of Neural Networks and Deep Learning
  • Neural Networks
    • The Biological Neuron
      • Synapses
      • Dendrites
      • Axons
      • Information flow across the biological neuron
      • From biological to artificial
    • The Perceptron
      • History of the perceptron
      • Definition of the perceptron
      • The perceptron learning algorithm
      • Limitations of the early perceptron
    • Multilayer Feed-Forward Networks
      • Evolution of the artificial neuron
        • Artificial neuron input
        • Connection weights
        • Biases
        • Activation functions
      • Comparing the biological neuron and the artificial neuron
      • Feed-forward neural network architecture
        • Input layer
        • Hidden layer
        • Output layer
        • Connections between layers
  • Training Neural Networks
    • Backpropagation Learning
      • Algorithm intuition
      • A closer look at backpropagation
      • Understanding backpropagation pseudocode
        • Updating the output layer weights
        • Further expressing the error term
        • The new propagation rule for the error value
        • Updating the hidden layers
  • Activation Functions
    • Linear
    • Sigmoid
    • Tanh
    • Hard Tanh
    • Softmax
    • Rectified Linear
      • Leaky ReLU
      • Softplus
  • Loss Functions
    • Loss Function Notation
    • Loss Functions for Regression
      • Mean squared error loss
      • Other loss functions for regression
        • Mean absolute error loss
        • Mean squared log error loss
        • Mean absolute percentage error loss
      • Regression loss function discussion
    • Loss Functions for Classification
      • Hinge loss
      • Logistic loss
        • Negative log likelihood
    • Loss Functions for Reconstruction
  • Hyperparameters
    • Learning Rate
    • Regularization
    • Momentum
    • Sparsity
• 3. Fundamentals of Deep Networks
  • Defining Deep Learning
    • What Is Deep Learning?
      • Defining deep networks
      • Evolutionary progress and resurgence
      • Advances in network architecture
        • Advances in layer types
        • Advances in neuron types
        • Hybrid architectures
      • From feature engineering to automated feature learning
        • Feature engineering
        • Feature learning
      • Generative modeling
        • Inceptionism
        • Modeling artistic style
        • GANs
        • Recurrent Neural Networks
      • The Tao of deep learning
    • Organization of This Chapter
  • Common Architectural Principles of Deep Networks
    • Parameters
    • Layers
    • Activation Functions
      • Activation functions for general architecture
        • Hidden layer activation functions
        • Output layer for regression
        • Output layer for binary classification
        • Output layer for multiclass classification
    • Loss Functions
      • Reconstruction cross-entropy
    • Optimization Algorithms
      • First-order methods
      • Second-order methods
        • L-BFGS
        • Conjugate gradient
        • Hessian-free
    • Hyperparameters
      • Layer size
      • Magnitude hyperparameters
        • Learning rate
        • Nesterovs momentum
        • AdaGrad
        • RMSProp
        • AdaDelta
        • ADAM
      • Regularization
        • Dropout
        • DropConnect
        • L1
        • L2
      • Mini-batching
    • Summary
  • Building Blocks of Deep Networks
    • RBMs
      • Network layout
        • Visible and hidden layers
        •  Connections and weights
        • Biases
      • Training
      • Reconstruction
      • Other uses of RBMs
    • Autoencoders
      • Similarities to multilayer perceptrons
      • Defining features of autoencoders
        • Unsupervised learning of unlabeled data
        • Learning to reproduce the input data
      • Training autoencoders
      • Common variants of autoencoders
        • Compression autoencoders
        • Denoising autoencoders
      • Applications of autoencoders
    • Variational Autoencoders
• 4. Major Architectures of Deep Networks
  • Unsupervised Pretrained Networks
    • Deep Belief Networks
      • Feature Extraction with RBM Layers
        • Learning higher-order features automatically
        • Initializing the feed-forward network
      • Fine-tuning a DBN with a feed-forward multilayer neural network
        • Gentle backpropagation
        • The output layer
      • Current state of DBNs
    • Generative Adversarial Networks
      • Training generative models, unsupervised learning, and GANs
        • The discriminator network
        • The generative network
      • Building generative models and Deep Convolutional Generative Adversarial Networks
      • Conditional GANs
      • Comparing GANs and variational autoencoders
  • Convolutional Neural Networks (CNNs)
    • Biological Inspiration
    • Intuition
    • CNN Architecture Overview
      • Neuron spatial arrangements
      • Evolution of the connections between layers
    • Input Layers
    • Convolutional Layers
      • Convolution
      • Filters
      • Activation maps
      • Parameter sharing
      • Learned filters and renders
      • ReLU activation functions as layers
      • Convolutional layer hyperparameters
        • Filter size
        • Output depth
        • Stride
        • Zero-padding
      • Batch normalization and layers
    • Pooling Layers
    • Fully Connected Layers
    • Other Applications of CNNs
    • CNNs of Note
    • Summary
  • Recurrent Neural Networks
    • Modeling the Time Dimension
      • Lost in time
      • Temporal feedback and loops in connections
      • Sequences and time-series data
      • Understanding model input and output
    • 3D Volumetric Input
      • Uneven time-series and masking
    • Why Not Markov Models?
    • General Recurrent Neural Network Architecture
      • Recurrent Neural Networks architecture and time-steps
    • LSTM Networks
      • Properties of LSTM networks
      • LSTM network architecture
      • LSTM units
      • LSTM layers
      • Training
      • BPTT and truncated BPTT
    • Domain-Specific Applications and Blended Networks
  • Recursive Neural Networks
    • Network Architecture
    • Varieties of Recursive Neural Networks
    • Applications of Recursive Neural Networks
  • Summary and Discussion
    • Will Deep Learning Make Other Algorithms Obsolete?
    • Different Problems Have Different Best Methods
    • When Do I Need Deep Learning?
      • When to use deep learning
      • When to stick with traditional machine learning
• 5. Building Deep Networks
  • Matching Deep Networks to the Right Problem
    • Columnar Data and Multilayer Perceptrons
    • Images and Convolutional Neural Networks
    • Time-series Sequences and Recurrent Neural Networks
    • Using Hybrid Networks
  • The DL4J Suite of Tools
    • Vectorization and DataVec
    • Runtimes and ND4J
      • ND4J and the need for speed
        • JavaCPP
        • CPU backends
        • GPU backends
      • Benchmarking ND4J and DL4J
  • Basic Concepts of the DL4J API
    • Loading and Saving Models
      • Writing a trained model to disk
        • Writing to HDFS
      • Reading a saved model from disk
        • Reading from HDFS
    • Getting Input for the Model
      • Loading data during training
    • Setting Up Model Architecture
      • Building layer-oriented architectures
      • Hyperparameters
    • Training and Evaluation
      • Making a prediction
      • Training, validation, and test data
  • Modeling CSV Data with Multilayer Perceptron Networks
    • Setting Up Input Data
    • Determining Network Architecture
      • General hyperparameters
      • First hidden layer
      • Output layer for classification
    • Training the Model
    • Evaluating the Model
  • Modeling Handwritten Images Using CNNs
    • Java Code Listing for the LeNet CNN
    • Loading and Vectorizing the Input Images
    • Network Architecture for LeNet in DL4J
      • General hyperparameters
      • Convolution layers
      • Max-pooling layers
      • Output layer
    • Training the CNN
  • Modeling Sequence Data by Using Recurrent Neural Networks
    • Generating Shakespeare via LSTMs
      • High-level modeling workflow
      • Java code for modeling Shakespeare
      • Setting up input data and vectorization
      • LSTM network architecture
        • General comments on hyperparameters
      • Training the LSTM network
      • Generating Shakespeare samples
    • Classifying Sensor Time-series Sequences Using LSTMs
      • Java code listing for recurrent classification example
      • Setting up input data and vectorization
      • Network architecture and training
  • Using Autoencoders for Anomaly Detection
    • Java Code Listing for Autoencoder Example
    • Setting Up Input Data
    • Autoencoder Network Architecture and Training
    • Evaluating the Model
  • Using Variational Autoencoders to Reconstruct MNIST Digits
    • Code Listing to Reconstruct MNIST Digits
    • Examining the VAE Model
      • Understanding the scatterplot
      • Understanding the generated images
  • Applications of Deep Learning in Natural Language Processing
    • Learning Word Embedding Using Word2Vec
      • The Word2Vec model and algorithm
      • Modeling context
      • Learning similar meaning and semantic relationships
      • Vector arithmetic and word embedding
      • Java code listing for Word2Vec example
      • Understanding the Word2Vec example
      • Other practical uses of Word2Vec
    • Distributed Representations of Sentences with Paragraph Vectors
      • Building paragraph vectors
      • Understanding the paragraph vectors example
    • Using Paragraph Vectors for Document Classification
      • Understanding the paragraph vectors classification example
      • Further exploration of the Word2Vec approach
        • Extensions into specific domains: Gov2Vec
        • Graphs and Node2Vec
        • Recommendation engines and Item2Vec
        • Computer vision and FaceNet
• 6. Tuning Deep Networks
  • Basic Concepts in Tuning Deep Networks
    • An Intuition for Building Deep Networks
    • Building the Intuition as a Step-by-Step Process
  • Matching Input Data and Network Architectures
    • Summary
  • Relating Model Goal and Output Layers
    • Regression Model Output Layer
    • Classification Model Output Layer
      • Single-label classification models
      • Models with more than two labels
        • Multiclass classification models
        • Multilabel classification models
  • Working with Layer Count, Parameter Count, and Memory
    • Feed-Forward Multilayer Neural Networks
      • Determining hidden-layer count
      • Determining neuron count per layer
    • Controlling Layer and Parameter Counts
      • Getting the parameter count for a network
    • Estimating Network Memory Requirements
  • Weight Initialization Strategies
  • Using Activation Functions
    • Summary Table for Activation Functions
  • Applying Loss Functions
  • Understanding Learning Rates
    • Using the Ratio of Updates-to-Parameters
    • Specific Recommendations for Learning Rates
  • How Sparsity Affects Learning
  • Applying Methods of Optimization
    • SGD Best Practices
  • Using Parallelization and GPUs for Faster Training
    • Online Learning and Parallel Iterative Algorithms
      • Task parallelism
      • Data parallelism
    • Parallelizing SGD in DL4J
      • Parallel SGD execution
    • GPUs
  • Controlling Epochs and Mini-Batch Size
    • Understanding Mini-Batch Size Trade-Offs
  • How to Use Regularization
    • Priors as Regularizers
    • Max-Norm Regularization
    • Dropout
      • Issues with dropout
    • Other Regularization Topics
  • Working with Class Imbalance
    • Methods for Sampling Classes
    • Weighted Loss Functions
  • Dealing with Overfitting
  • Using Network Statistics from the Tuning UI
    • Detecting Poor Weight Initialization
    • Detecting Nonshuffled Data
    • Detecting Issues with Regularization
• 7. Tuning Specific Deep Network Architectures
  • Convolutional Neural Networks (CNNs)
    • Common Convolutional Architectural Patterns
    • Configuring Convolutional Layers
      • Setting the stride for filters
      • Using padding
      • Choosing the number of filters
      • Configuring filter size
      • Convolution mode and calculating spatial size of output volume
    • Configuring Pooling Layers
    • Transfer Learning
      • An alternative to training from scratch
      • When to consider trying transfer learning
  • Recurrent Neural Networks
    • Network Input Data and Input Layers
    • Output Layers and RnnOutputLayer
    • Training the Network
      • Initializing weights
      • Backpropagation through time
      • Regularization
    • Debugging Common Issues with LSTMs
    • Padding and Masking
      • Applying padding and masking to volumetric input
    • Evaluation and Scoring With Masking
      • Classification using the evaluation class
      • Scoring new data with MultiLayerNetwork
    • Variants of Recurrent Network Architectures
  • Restricted Boltzmann Machines
    • Hidden Units and Modeling Available Information
    • Using Different Units
    • Using Regularization with RBMs
  • DBNs
    • Using Momentum
    • Using Regularization
      • Dropout
    • Determining Hidden Unit Count
• 8. Vectorization
  • Introduction to Vectorization in Machine Learning
    • Why Do We Need to Vectorize Data?
    • Strategies for Dealing with Columnar Raw Data Attributes
      • Nominal
      • Ordinal
      • Interval
      • Ratio
    • Feature Engineering and Normalization Techniques
      • Feature copying
      • Normalization
        • Standardization and zero mean, unit variance
        • Min-max scaling
        • Whitening and principal component analysis
        • Applying normalization in Recurrent Neural Networks and CNNs
        • Normalization for regression models
      • Binarization
  • Using DataVec for ETL and Vectorization
  • Vectorizing Image Data
    • Image Data Representation in DL4J
    • Image Data and Vector Normalization with DataVec
  • Working with Sequential Data in Vectorization
    • Major Variations of Sequential Data Sources
    • Vectorizing Sequential Data with DataVec
      • Converting time-series to a single vector
      • Converting sequential data to a DataSet object in local mode
      • Building custom DataSets from sequential data
  • Working with Text in Vectorization
    • Bag of Words
    • TF-IDF
      • TF
      • IDF
      • Computing the full TF-IDF score
    • Comparing Word2Vec and VSM Comparison
  • Working with Graphs
• 9. Using Deep Learning and DL4J on Spark
  • Introduction to Using DL4J with Spark and Hadoop
    • Operating Spark from the Command Line
      • spark-submit
      • Working with Hadoop security and Kerberos
        • Uploading the Spark assembly
        • Initializing Kerberos
  • Configuring and Tuning Spark Execution
    • Running Spark on Mesos
    • Running Spark on YARN
      • Comparing Spark execution modes
    • General Spark Tuning Guide
      • Setting the number of executors
      • Spark executors and CPU cores
      • Spark executors and memory
      • Spark and YARN container resource allocation
      • Understanding executor memory requests in YARN
      • Understanding Spark, the JVM, and garbage collection
        • Dealing with slowing garbage collection efficiency or pauses
        • Selecting a garbage collector for the JVM and Spark
    • Tuning DL4J Jobs on Spark
      • Tuning the number of executors
      • Tuning the amount of memory for executors
  • Setting Up a Maven Project Object Model for Spark and DL4J
    • A pom.xml File Dependency Template
    • Setting Up a POM File for CDH 5.X
    • Setting Up a POM File for HDP 2.4
  • Troubleshooting Spark and Hadoop
    • Common Issues with ND4J
      • ND4J and Kyro serialization
      • jnind4j and java.library.path
  • DL4J Parallel Execution on Spark
    • A Minimal Spark Training Example
  • DL4J API Best Practices for Spark
  • Multilayer Perceptron Spark Example
    • Setting Up MLP Network Architecture for Spark
    • Distributed Training and Model Evaluation
    • Building and Executing a DL4J Spark Job
  • Generating Shakespeare Text with Spark and Long Short-Term Memory
    • Setting Up the LSTM Network Architecture
    • Training, Tracking Progress, and Understanding Results
  • Modeling MNIST with a Convolutional Neural Network on Spark
    • Configuring the Spark Job and Loading MNIST Data
    • Setting Up the LeNet CNN Architecture and Training
• A. What Is Artificial Intelligence?
  • The Story So Far
    • Defining Deep Learning
    • Defining Artificial Intelligence
      • The study of intelligence
      • Cognitive dissonance and modern definitions
        • What AI is not
        • Moving the goal posts
        • Segmenting the definitions of AI
        • Critical commentary on segments
        • A fifth aspirational definition of AI
      • The AI winters
        • AI Winter I: (19741980)
        • AI Winter II: Late 1980s
      • The common patterns of AI Winters
  • What Is Driving Interest Today in AI Today?
  • Winter Is Coming
• B. RL4J and Reinforcement Learning
  • Preliminaries
    • Markov Decision Process
    • Terminology
  • Different Settings
    • Model-Free
    • Observation Setting
    • Single-Player and Adversarial Games
  • Q-Learning
    • From Policy to Neural Networks the following
    • Policy Iteration
    • Exploration Versus Exploitation
    • Bellman Equation
    • Initial State Sampling
    • Q-Learning Implementation
    • Modeling Q(s,a)
    • Experience Replay
      • Compression
    • Convolutional Layers and Image Preprocessing
      • Image processing
    • History Processing
    • Double Q-Learning
    • Clipping
    • Scaling Rewards
    • Prioritized Replay
  • Graph, Visualization, and Mean-Q
  • RL4J
  • Conclusion
• C. Numbers Everyone Should Know
• D. Neural Networks and Backpropagation: A Mathematical Approach
  • Introduction
  • Backpropagation in a Multilayer Perceptron
• E. Using the ND4J API
  • Design and Basic Usage
    • Understanding NDArrays
    • ND4J General Syntax
    • The Basics of Working with NDArrays
      • The ND4J class
        • Nd4j.zeros( int ... )
        • Nd4j.ones( int ... )
        • Initializing with other values
        • Initializing with random numbers
      • Controlling the shape of NDArrays
      • Creating basic arrays
        • Example: create a 2 x 2 NDArray
        • Example: add two 2 x 2 NDArrays together
      • Creating NDArrays from Java arrays
      • Getting and setting individual NDArray values
      • Working with NDArray rows
        • Get a single row
        • Get multiple rows
        • Setting a single row
      • Quick reference for determining the size/dimensions of NDArrays
    • Dataset
      • Relationship to NDArray
      • Common uses
  • Creating Input Vectors
    • Basics of Vector Creation
      • Sizing the vector
      • Setting feature values
      • Setting the label
        • Single-label output
        • Multiple-label output
        • Regression output
  • Using MLLibUtil
    • Converting from INDArray to MLLib Vector
    • Converting from MLLib Vector to INDArray
  • Making Model Predictions with DL4J
    • Using the DL4J and ND4J Together
      • Differences between output vector depending on output layer type
        • Logistic output layer for binary classification
        • Softmax output layer for multilabel classification
        • Linear output layer for regression output
      • Getting the redicted label from the returned INDArray
• F. Using DataVec
  • Loading Data for Machine Learning
  • Loading CSV Data for Multilayer Perceptrons
  • Loading Image Data for Convolutional Neural Networks
  • Loading Sequence Data for Recurrent Neural Networks
  • Transforming Data: Data Wrangling with DataVec
    • DataVec Transforms: Key Concepts
    • DataVec Transform Functionality: An Example
• G. Working with DL4J from Source
  • Verifying Git Is Installed
  • Cloning Key DL4J GitHub Projects
  • Downloading Source via Zip File
  • Using Maven to Build Source Code
• H. Setting Up DL4J Projects
  • Creating a New DL4J Project
    • Java
    • Working with Maven
      • A minimal Project Object Model file
        • Project Object Model explanation
    • IDEs
      • Quickstart a DL4J project by using IntelliJ
  • Setting Up Other Maven POMs
    • ND4J and Maven
• I. Setting Up GPUs for DL4J Projects
  • Switching Backends to GPU
    • Picking a GPU
    • Training on a Multiple GPU System
  • CUDA on Different Platforms
  • Monitoring GPU Performance
    • NVIDIA System Management Interface
• J. Troubleshooting DL4J Installations
  • Previous Installation
  • Memory Errors When Installing From Source
  • Older Versions of Maven
  • Maven and PATH Variables
  • Bad JDK Versions
  • C++ and Other Development Tools
  • Windows and Include Paths
  • Monitoring GPUs
  • Using the JVisualVM
  • Working with Clojure
  • OS X and Float Support
  • Fork-Join Bug in Java 7
  • Precautions
    • Other Local Repositories
    • Check Maven Dependencies
    • Reinstall Dependencies
    • If All Else Fails
  • Different Platforms
    • OS X
    • Windows
      • Setting up Visual Studio
      • Working with Windows on 64-bit platforms
    • Linux
      • Ubuntu
      • Centos
• Index

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