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Contents:

  • Part I: Applied Math and Machine Learning Basics
  • Part II: Modern Practical Deep Networks
    • 8 Optimization for Training Deep Models
    • 9 The convolutional Networks
      • 9.1 The Convolutional Operation
      • 9.2 Motivation
      • 9.3 Pooling
      • 9.4 Convolution and Pooling as a Infinitely Strong Prior
      • 9.5 Variants of the Basic Convolution Function
      • 9.6 Structured Output
      • 9.7 Data Type
      • 9.8 Efficient Convolution Algorithms
      • 9.9 Random or Unsupervised Features
    • 11 Practical Methodoloogy
    • 12 Applications
  • Part III: Deep Learning Research
  • Extra
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  • Part II: Modern Practical Deep Networks »
  • 9 The convolutional Networks
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9 The convolutional NetworksΒΆ

  • 9.1 The Convolutional Operation
  • 9.2 Motivation
  • 9.3 Pooling
  • 9.4 Convolution and Pooling as a Infinitely Strong Prior
  • 9.5 Variants of the Basic Convolution Function
  • 9.6 Structured Output
  • 9.7 Data Type
  • 9.8 Efficient Convolution Algorithms
  • 9.9 Random or Unsupervised Features
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