H2O World 2014 Training
Introduction
1.
H2O World Training Sandbox
2.
H2O in Big Data Environments
3.
Hands-On Training
3.1.
H2O with the Web UI
3.2.
R with H2O
3.3.
Supervised Learning
3.3.1.
Generalized Linear Models
3.3.2.
Gradient Boosted Models
3.3.3.
Random Forests
3.3.4.
Regression
3.3.5.
Classification
3.3.6.
Deep Learning
3.4.
Unsupervised Learning
3.4.1.
KMeans Clustering
3.4.2.
Dimensionality Reduction
3.4.3.
Anomaly Detection
3.5.
Advanced Topics
3.5.1.
Multi-model Parameter Tuning
3.5.2.
Categorical Feature Engineering
3.5.3.
Other Useful Tools
3.6.
Practical Use Cases for Marketing
4.
Sparkling Water
5.
Python on H2O
6.
Demos
6.1.
Tableau
6.2.
Excel
6.3.
Streaming Data
7.
Build Applications on Top of H2O
7.1.
KMeans
7.2.
Grep
7.3.
Quantiles
7.4.
Build with Sparkling Water
8.
Troubleshooting
9.
More Information
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H2O World 2014 Training
Unsupervised Learning
Arno Candel
&
Spencer Aiello
:
K-Means Clustering
Arno Candel
:
Dimensionality Reduction on MNIST
Arno Candel
:
Anomaly Detection on MNIST with H2O Deep Learning