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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
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Part II: Modern Practical Deep Networks
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9 The convolutional Networks
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9 The convolutional Networks
ΒΆ
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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