reklama - zainteresowany?

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

Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems
ebook
Autor: Aurélien Géron
ISBN: 9781491962244
stron: 576, Format: ebook
Data wydania: 2017-03-13
Ksigarnia: Helion

Cena ksiki: 169,15 z (poprzednio: 196,69 z)
Oszczdzasz: 14% (-27,54 z)

Dodaj do koszyka Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use scikit-learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details

Dodaj do koszyka Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems

 

Osoby ktre kupoway "Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems", wybieray take:

  • Windows Small Business Server 2003. Administracja systemem

Dodaj do koszyka Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems

Spis treci

Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems eBook -- spis treci

  • 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

Dodaj do koszyka Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems

Code, Publish & WebDesing by CATALIST.com.pl



(c) 2005-2019 CATALIST agencja interaktywna, znaki firmowe nale do wydawnictwa Helion S.A.