ImageNet Classification with Deep Convolutional Neural Networks

1 Introduction

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2 The Dataset

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CODE

def func(variable):
    return 'hello, world'

def func(variable):
    return 'hello, world'

def print_hi(name)
  puts "Hi, #{name}"
end
print_hi('Tom')
#=> prints 'Hi, Tom' to STDOUT.

3 The Architecture

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3.1 ReLU Nonlinearity

3.2 Training on Multiple GPUs

3.3 Local Response Normalization

3.4 Overlapping Pooling

3.5 Overall Architecture

4 Reducing Overfitting

4.1 Data Augmentation

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4.2 Dropout

5 Details of learning

6 Results

6.1 Qualitative Evaluations

7 Discussion

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