Deep Learning Booklet
Deep Learning Booklet
Deep Learning Booklet

Last updated on 2015-07-17

About the Book

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About the Author

H2O.ai
H2O.ai

Author Biographies

Viraj Parmar

Viraj is currently an undergraduate at Princeton studying applied mathematics. Prior to joining H2O.ai as a data and math hacker intern, Viraj worked in a research group at the MIT Center for Technology and Design. His interests are in software engineering and large-scale machine learning. Apart from work, Viraj enjoys reading, sampling new cuisines, and traveling with his family.

Arno Candel

Arno is a Physicist & Hacker at H2O.ai. Prior to that, he was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite- element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and is a sought-after conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named "2014 Big Data All-Star" by Fortune Magazine.

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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