Python Deep Learning

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• Python Deep Learning
  • Table of Contents
  • Python Deep Learning
  • Credits
  • About the Authors
  • About the Reviewer
  • www.PacktPub.com
    • eBooks, discount offers, and more
      • Why subscribe?
  • Customer Feedback
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Downloading the color images of this book
    • Errata
      • Piracy
      • Questions
  • 1. Machine Learning An Introduction
    • What is machine learning?
    • Different machine learning approaches
      • Supervised learning
      • Unsupervised learning
      • Reinforcement learning
      • Steps Involved in machine learning systems
      • Brief description of popular techniques/algorithms
        • Linear regression
        • Decision trees
        • K-means
        • Nave Bayes
        • Support vector machines
        • The cross-entropy method
        • Neural networks
        • Deep learning
      • Applications in real life
      • A popular open source package
    • Summary
  • 2. Neural Networks
    • Why neural networks?
    • Fundamentals
      • Neurons and layers
      • Different types of activation function
      • The back-propagation algorithm
        • Linear regression
        • Logistic regression
        • Back-propagation
      • Applications in industry
        • Signal processing
        • Medical
        • Autonomous car driving
        • Business
        • Pattern recognition
        • Speech production
      • Code example of a neural network for the function xor
    • Summary
  • 3. Deep Learning Fundamentals
    • What is deep learning?
      • Fundamental concepts
      • Feature learning
      • Deep learning algorithms
    • Deep learning applications
      • Speech recognition
      • Object recognition and classification
    • GPU versus CPU
    • Popular open source libraries an introduction
      • Theano
      • TensorFlow
      • Keras
      • Sample deep neural net code using Keras
    • Summary
  • 4. Unsupervised Feature Learning
    • Autoencoders
      • Network design
      • Regularization techniques for autoencoders
        • Denoising autoencoders
        • Contractive autoencoders
        • Sparse autoencoders
      • Summary of autoencoders
    • Restricted Boltzmann machines
      • Hopfield networks and Boltzmann machines
      • Boltzmann machine
      • Restricted Boltzmann machine
      • Implementation in TensorFlow
      • Deep belief networks
    • Summary
  • 5. Image Recognition
    • Similarities between artificial and biological models
    • Intuition and justification
    • Convolutional layers
      • Stride and padding in convolutional layers
    • Pooling layers
    • Dropout
    • Convolutional layers in deep learning
    • Convolutional layers in Theano
    • A convolutional layer example with Keras to recognize digits
    • A convolutional layer example with Keras for cifar10
    • Pre-training
    • Summary
  • 6. Recurrent Neural Networks and Language Models
    • Recurrent neural networks
      • RNN how to implement and train
        • Backpropagation through time
        • Vanishing and exploding gradients
      • Long short term memory
    • Language modeling
      • Word-based models
        • N-grams
        • Neural language models
      • Character-based model
        • Preprocessing and reading data
        • LSTM network
        • Training
        • Sampling
        • Example training
    • Speech recognition
      • Speech recognition pipeline
      • Speech as input data
      • Preprocessing
      • Acoustic model
        • Deep belief networks
        • Recurrent neural networks
        • CTC
        • Attention-based models
      • Decoding
      • End-to-end models
    • Summary
    • Bibliography
  • 7. Deep Learning for Board Games
    • Early game playing AI
    • Using the min-max algorithm to value game states
    • Implementing a Python Tic-Tac-Toe game
    • Learning a value function
    • Training AI to master Go
    • Upper confidence bounds applied to trees
    • Deep learning in Monte Carlo Tree Search
    • Quick recap on reinforcement learning
    • Policy gradients for learning policy functions
    • Policy gradients in AlphaGo
    • Summary
  • 8. Deep Learning for Computer Games
    • A supervised learning approach to games
    • Applying genetic algorithms to playing games
    • Q-Learning
      • Q-function
    • Q-learning in action
    • Dynamic games
      • Experience replay
      • Epsilon greedy
    • Atari Breakout
      • Atari Breakout random benchmark
      • Preprocessing the screen
      • Creating a deep convolutional network
      • Convergence issues in Q-learning
      • Policy gradients versus Q-learning
    • Actor-critic methods
      • Baseline for variance reduction
      • Generalized advantage estimator
    • Asynchronous methods
    • Model-based approaches
    • Summary
  • 9. Anomaly Detection
    • What is anomaly and outlier detection?
    • Real-world applications of anomaly detection
    • Popular shallow machine learning techniques
      • Data modeling
      • Detection modeling
    • Anomaly detection using deep auto-encoders
    • H2O
      • Getting started with H2O
    • Examples
      • MNIST digit anomaly recognition
        • Electrocardiogram pulse detection
    • Summary
  • 10. Building a Production-Ready Intrusion Detection System
    • What is a data product?
    • Training
      • Weights initialization
      • Parallel SGD using HOGWILD!
      • Adaptive learning
        • Rate annealing
        • Momentum
        • Nesterovs acceleration
        • Newton's method
        • Adagrad
        • Adadelta
      • Distributed learning via Map/Reduce
      • Sparkling Water
    • Testing
    • Model validation
      • Labeled Data
      • Unlabeled Data
      • Summary of validation
    • Hyper-parameters tuning
    • End-to-end evaluation
      • A/B Testing
      • A summary of testing
    • Deployment
      • POJO model export
      • Anomaly score APIs
      • A summary of deployment
    • Summary
  • Index

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