Deep neural networks

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Table of contents

1 Introduction 
1.1 Problem Statement
1.2 Scope
1.3 Outline
2 Background 
2.1 Machine learning
2.1.1 Supervised learning
2.2 Artificial neural networks
2.3 Neural network training
2.4 Deep neural networks
2.4.1 Backpropagation
2.4.2 The activation function
2.5 Regression
2.6 Overfitting
2.7 TensorFlow
3 Method 
3.1 Measurement of error
3.2 Gathering data
3.2.1 Fixed parameters
3.2.2 Variable parameters
3.2.3 Collection script
3.2.4 TensorFlow installation
3.3 Our deep neural network
3.3.1 Finding an accurate configuration
3.3.2 Training and evaluation sets
4 Results 
4.1 Time variation distribution
4.1.1 An upper limit on accuracy
4.2 Looking for patterns
4.3 Resulting neural network
4.3.1 Accuracy
5 Discussion 
5.1 Pre-processing data
5.2 Splitting the data
5.3 Future research
6 Conclusion 
Bibliography