Deep Learning Booklet
Deep Learning Booklet
About the Book
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About the Author
Table of Contents
- What is H2O?
-
Introduction
- Installation
- Support
- Deep Learning Overview
-
H2O’s Deep Learning Architecture
- Summary of Features
- Training Protocol
- Regularization
- Advanced Optimization
- Loading Data
- Additional Parameters
-
Use Case: MNIST Digit Classification
- MNIST Overview
- Performing a Trial Run
- Web Interface
- Grid Search for Model Comparison
- Checkpoint Models
- Achieving World Record Performance
-
Deep Autoencoders
- Nonlinear Dimensionality Reduction
- Use Case: Anomaly Detection
- Appendix A: Complete Parameter List
- Appendix B: References
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