Contents

Section Title Page
1 Introduction 5
2 What is H2O? 5
3 Installation 6
3.1 Installation in R 7
3.2 Installation in Python 7
3.3 Pointing to a Different H2O Cluster 8
3.4 Example Code 8
3.5 Citation 9
4 Deep Learning Overview 9
5 H2O’s Deep Learning Architecture 10
5.1 Summary of Features 11
5.2 Training Protocol 12
5.2.1 Initialization 12
5.2.2 Activation and Loss Functions 12
5.2.3 Parallel Distributed Network Training 15
5.2.4 Specifying the Number of Training Samples 17
5.3 Regularization 18
5.4 Advanced Optimization 18
5.4.1 Momentum Training 19
5.4.2 Rate Annealing 19
5.4.3 Adaptive Learning 20
5.5 Loading Data 20
5.5.1 Data Standardization/Normalization 20
5.5.2 Convergence-based Early Stopping 21
5.5.3 Time-based Early Stopping 21
5.6 Additional Parameters 21
6 Use Case: MNIST Digit Classification 22
6.1 MNIST Overview 22
6.2 Performing a Trial Run 25
6.2.1 N-fold Cross-Validation 27
6.2.2 Extracting and Handling the Results 28
6.3 Web Interface 31
6.3.1 Variable Importances 31
6.3.2 Java Model 33
6.4 Grid Search for Model Comparison 33
6.4.1 Cartesian Grid Search 34
6.4.2 Random Grid Search 35
6.5 Checkpoint Models 37
6.6 Achieving World-Record Performance 41
6.7 Computational Performance 41
7 Deep Autoencoders 42
7.1 Nonlinear Dimensionality Reduction 42
7.2 Use Case: Anomaly Detection 43
7.2.1 Stacked Autoencoder 46
7.2.2 Unsupervised Pretraining with Supervised Fine-Tuning 46
8 Parameters 46
9 Common R Commands 53
10 Common Python Commands 53
11 Acknowledgments 53
12 References 54
13 Authors 55

 

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