Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems AurĂŠlien GĂŠron

Kup polskie wydanie:

Spis treści książki

• Preface
  • The Machine Learning Tsunami
  • Machine Learning in Your Projects
  • Objective and Approach
  • Prerequisites
  • Roadmap
  • Other Resources
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• I. The Fundamentals of Machine Learning
• 1. The Machine Learning Landscape
  • What Is Machine Learning?
  • Why Use Machine Learning?
  • Types of Machine Learning Systems
    • Supervised/Unsupervised Learning
      • Supervised learning
      • Unsupervised learning
      • Semisupervised learning
      • Reinforcement Learning
    • Batch and Online Learning
      • Batch learning
      • Online learning
    • Instance-Based Versus Model-Based Learning
      • Instance-based learning
      • Model-based learning
  • Main Challenges of Machine Learning
    • Insufficient Quantity of Training Data
    • Nonrepresentative Training Data
    • Poor-Quality Data
    • Irrelevant Features
    • Overfitting the Training Data
    • Underfitting the Training Data
    • Stepping Back
  • Testing and Validating
  • Exercises
• 2. End-to-End Machine Learning Project
  • Working with Real Data
  • Look at the Big Picture
    • Frame the Problem
    • Select a Performance Measure
    • Check the Assumptions
  • Get the Data
    • Create the Workspace
    • Download the Data
    • Take a Quick Look at the Data Structure
    • Create a Test Set
  • Discover and Visualize the Data to Gain Insights
    • Visualizing Geographical Data
    • Looking for Correlations
    • Experimenting with Attribute Combinations
  • Prepare the Data for Machine Learning Algorithms
    • Data Cleaning
    • Handling Text and Categorical Attributes
    • Custom Transformers
    • Feature Scaling
    • Transformation Pipelines
  • Select and Train a Model
    • Training and Evaluating on the Training Set
    • Better Evaluation Using Cross-Validation
  • Fine-Tune Your Model
    • Grid Search
    • Randomized Search
    • Ensemble Methods
    • Analyze the Best Models and Their Errors
    • Evaluate Your System on the Test Set
  • Launch, Monitor, and Maintain Your System
  • Try It Out!
  • Exercises
• 3. Classification
  • MNIST
  • Training a Binary Classifier
  • Performance Measures
    • Measuring Accuracy Using Cross-Validation
    • Confusion Matrix
    • Precision and Recall
    • Precision/Recall Tradeoff
    • The ROC Curve
  • Multiclass Classification
  • Error Analysis
  • Multilabel Classification
  • Multioutput Classification
  • Exercises
• 4. Training Models
  • Linear Regression
    • The Normal Equation
    • Computational Complexity
  • Gradient Descent
    • Batch Gradient Descent
    • Stochastic Gradient Descent
    • Mini-batch Gradient Descent
  • Polynomial Regression
  • Learning Curves
  • Regularized Linear Models
    • Ridge Regression
    • Lasso Regression
    • Elastic Net
    • Early Stopping
  • Logistic Regression
    • Estimating Probabilities
    • Training and Cost Function
    • Decision Boundaries
    • Softmax Regression
  • Exercises
• 5. Support Vector Machines
  • Linear SVM Classification
    • Soft Margin Classification
  • Nonlinear SVM Classification
    • Polynomial Kernel
    • Adding Similarity Features
    • Gaussian RBF Kernel
    • Computational Complexity
  • SVM Regression
  • Under the Hood
    • Decision Function and Predictions
    • Training Objective
    • Quadratic Programming
    • The Dual Problem
    • Kernelized SVM
    • Online SVMs
  • Exercises
• 6. Decision Trees
  • Training and Visualizing a Decision Tree
  • Making Predictions
  • Estimating Class Probabilities
  • The CART Training Algorithm
  • Computational Complexity
  • Gini Impurity or Entropy?
  • Regularization Hyperparameters
  • Regression
  • Instability
  • Exercises
• 7. Ensemble Learning and Random Forests
  • Voting Classifiers
  • Bagging and Pasting
    • Bagging and Pasting in Scikit-Learn
    • Out-of-Bag Evaluation
  • Random Patches and Random Subspaces
  • Random Forests
    • Extra-Trees
    • Feature Importance
  • Boosting
    • AdaBoost
    • Gradient Boosting
  • Stacking
  • Exercises
• 8. Dimensionality Reduction
  • The Curse of Dimensionality
  • Main Approaches for Dimensionality Reduction
    • Projection
    • Manifold Learning
  • PCA
    • Preserving the Variance
    • Principal Components
    • Projecting Down to d Dimensions
    • Using Scikit-Learn
    • Explained Variance Ratio
    • Choosing the Right Number of Dimensions
    • PCA for Compression
    • Incremental PCA
    • Randomized PCA
  • Kernel PCA
    • Selecting a Kernel and Tuning Hyperparameters
  • LLE
  • Other Dimensionality Reduction Techniques
  • Exercises
• II. Neural Networks and Deep Learning
• 9. Up and Running with TensorFlow
  • Installation
  • Creating Your First Graph and Running It in a Session
  • Managing Graphs
  • Lifecycle of a Node Value
  • Linear Regression with TensorFlow
  • Implementing Gradient Descent
    • Manually Computing the Gradients
    • Using autodiff
    • Using an Optimizer
  • Feeding Data to the Training Algorithm
  • Saving and Restoring Models
  • Visualizing the Graph and Training Curves Using TensorBoard
  • Name Scopes
  • Modularity
  • Sharing Variables
  • Exercises
• 10. Introduction to Artificial Neural Networks
  • From Biological to Artificial Neurons
    • Biological Neurons
    • Logical Computations with Neurons
    • The Perceptron
    • Multi-Layer Perceptron and Backpropagation
  • Training an MLP with TensorFlows High-Level API
  • Training a DNN Using Plain TensorFlow
    • Construction Phase
    • Execution Phase
    • Using the Neural Network
  • Fine-Tuning Neural Network Hyperparameters
    • Number of Hidden Layers
    • Number of Neurons per Hidden Layer
    • Activation Functions
  • Exercises
• 11. Training Deep Neural Nets
  • Vanishing/Exploding Gradients Problems
    • Xavier and He Initialization
    • Nonsaturating Activation Functions
    • Batch Normalization
      • Implementing Batch Normalization with TensorFlow
    • Gradient Clipping
  • Reusing Pretrained Layers
    • Reusing a TensorFlow Model
    • Reusing Models from Other Frameworks
    • Freezing the Lower Layers
    • Caching the Frozen Layers
    • Tweaking, Dropping, or Replacing the Upper Layers
    • Model Zoos
    • Unsupervised Pretraining
    • Pretraining on an Auxiliary Task
  • Faster Optimizers
    • Momentum optimization
    • Nesterov Accelerated Gradient
    • AdaGrad
    • RMSProp
    • Adam Optimization
    • Learning Rate Scheduling
  • Avoiding Overfitting Through Regularization
    • Early Stopping
    • 1 and 2 Regularization
    • Dropout
    • Max-Norm Regularization
    • Data Augmentation
  • Practical Guidelines
  • Exercises
• 12. Distributing TensorFlow Across Devices and Servers
  • Multiple Devices on a Single Machine
    • Installation
    • Managing the GPU RAM
    • Placing Operations on Devices
      • Simple placement
      • Logging placements
      • Dynamic placement function
      • Operations and kernels
      • Soft placement
    • Parallel Execution
    • Control Dependencies
  • Multiple Devices Across Multiple Servers
    • Opening a Session
    • The Master and Worker Services
    • Pinning Operations Across Tasks
    • Sharding Variables Across Multiple Parameter Servers
    • Sharing State Across Sessions Using Resource Containers
    • Asynchronous Communication Using TensorFlow Queues
      • Enqueuing data
      • Dequeuing data
      • Queues of tuples
      • Closing a queue
      • RandomShuffleQueue
      • PaddingFifoQueue
    • Loading Data Directly from the Graph
      • Preload the data into a variable
      • Reading the training data directly from the graph
      • Multithreaded readers using a Coordinator and a QueueRunner
      • Other convenience functions
  • Parallelizing Neural Networks on a TensorFlow Cluster
    • One Neural Network per Device
    • In-Graph Versus Between-Graph Replication
    • Model Parallelism
    • Data Parallelism
      • Synchronous updates
      • Asynchronous updates
      • Bandwidth saturation
      • TensorFlow implementation
  • Exercises
• 13. Convolutional Neural Networks
  • The Architecture of the Visual Cortex
  • Convolutional Layer
    • Filters
    • Stacking Multiple Feature Maps
    • TensorFlow Implementation
    • Memory Requirements
  • Pooling Layer
  • CNN Architectures
    • LeNet-5
    • AlexNet
    • GoogLeNet
    • ResNet
  • Exercises
• 14. Recurrent Neural Networks
  • Recurrent Neurons
    • Memory Cells
    • Input and Output Sequences
  • Basic RNNs in TensorFlow
    • Static Unrolling Through Time
    • Dynamic Unrolling Through Time
    • Handling Variable Length Input Sequences
    • Handling Variable-Length Output Sequences
  • Training RNNs
    • Training a Sequence Classifier
    • Training to Predict Time Series
    • Creative RNN
  • Deep RNNs
    • Distributing a Deep RNN Across Multiple GPUs
    • Applying Dropout
    • The Difficulty of Training over Many Time Steps
  • LSTM Cell
    • Peephole Connections
  • GRU Cell
  • Natural Language Processing
    • Word Embeddings
    • An EncoderDecoder Network for Machine Translation
  • Exercises
• 15. Autoencoders
  • Efficient Data Representations
  • Performing PCA with an Undercomplete Linear Autoencoder
  • Stacked Autoencoders
    • TensorFlow Implementation
    • Tying Weights
    • Training One Autoencoder at a Time
    • Visualizing the Reconstructions
    • Visualizing Features
  • Unsupervised Pretraining Using Stacked Autoencoders
  • Denoising Autoencoders
    • TensorFlow Implementation
  • Sparse Autoencoders
    • TensorFlow Implementation
  • Variational Autoencoders
    • Generating Digits
  • Other Autoencoders
  • Exercises
• 16. Reinforcement Learning
  • Learning to Optimize Rewards
  • Policy Search
  • Introduction to OpenAI Gym
  • Neural Network Policies
  • Evaluating Actions: The Credit Assignment Problem
  • Policy Gradients
  • Markov Decision Processes
  • Temporal Difference Learning and Q-Learning
    • Exploration Policies
    • Approximate Q-Learning
  • Learning to Play Ms. Pac-Man Using Deep Q-Learning
  • Exercises
  • Thank You!
• A. Exercise Solutions
  • Chapter 1: The Machine Learning Landscape
  • Chapter 2: End-to-End Machine Learning Project
  • Chapter 3: Classification
  • Chapter 4: Training Linear Models
  • Chapter 5: Support Vector Machines
  • Chapter 6: Decision Trees
  • Chapter 7: Ensemble Learning and Random Forests
  • Chapter 8: Dimensionality Reduction
  • Chapter 9: Up and Running with TensorFlow
  • Chapter 10: Introduction to Artificial Neural Networks
  • Chapter 11: Training Deep Neural Nets
  • Chapter 12: Distributing TensorFlow Across Devices and Servers
  • Chapter 13: Convolutional Neural Networks
  • Chapter 14: Recurrent Neural Networks
  • Chapter 15: Autoencoders
  • Chapter 16: Reinforcement Learning
• B. Machine Learning Project Checklist
  • Frame the Problem and Look at the Big Picture
  • Get the Data
  • Explore the Data
  • Prepare the Data
  • Short-List Promising Models
  • Fine-Tune the System
  • Present Your Solution
  • Launch!
• C. SVM Dual Problem
• D. Autodiff
  • Manual Differentiation
  • Symbolic Differentiation
  • Numerical Differentiation
  • Forward-Mode Autodiff
  • Reverse-Mode Autodiff
• E. Other Popular ANN Architectures
  • Hopfield Networks
  • Boltzmann Machines
  • Restricted Boltzmann Machines
  • Deep Belief Nets
  • Self-Organizing Maps
• Index

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