H2O World 2015 - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

1. We l c o m e ' t o ' H 2 O ' Wo r l d
Sri'&'H2O'Team'

2. Data
Science
is
a
Team
Sport!
Culture
Matters!

3. Open
Source
Breeds
Courage!
Community
Matters!
Every
generation
needs
to
make
its
own
history!

4. Code
is
conversation
with
Customer!
Great
Product
Matters!

5. Accuracy
with
Speed
and
Scale
HDFS%
S3%
SQL%%
NoSQL%
CLASSIFICATION%
REGRESSION%
FEATURE%
ENGINEERING%
IN4MEMORY%
MAP%REDUCE/FORK%JOIN%
COLUMNAR%COMPRESSION%
DEEP%LEARNING%
PCA,%GLM,%COX%
RANDOM%FOREST%/%GBM%
ENSEMBLES%
FAST % M O D E L I NG % E NG I NE %
Streaming%
NANO % FAST % JAVA% S CO R I NG% E NGI NES %
MATRIX%
FACTORIZATION% CLUSTERING%
MUNGING%

6. What’s
New
in
H2O-­‐3
H2O-­‐3
vs
H2O-­‐2:
• Total
rewrite
of
the
core
in
Java:
built
for
data
scientists
AND
developers!
• Unique
Flow
GUI
(Notebook
and
more)
• REST
Schemas
for
self-­‐describing
API
for
all
methods/algos
• New
R
client:
cleaner,
faster
• Sparkling
Water:
H2O
is
the
Killer
App
on
Spark
• Fully
featured
Python
client
(incl.
Pipelines,
scikit-­‐learn
look&feel)
• New
expression
parser
&
backend
execution
engine
for
R,
Py,
Flow
• New
Algo:
GLRM
-­‐
Generalized
Low
Rank
Modeling
(unifies
PCA,
K-­‐Means,
Matrix
Factorization,
Imputation,
etc.)
• New
Solvers
for
GLM:
Coordinate
Descent
and
L-­‐BFGS
continued…

7. What’s
New
in
H2O-­‐3
Additional
New
Features:
• Grid
Search
for
all
Algorithms
(R/Py/Flow)
• N-­‐fold
Cross-­‐Validation
for
all
Algorithms
• Early
Stopping
(check
for
convergence)
for
GBM/DRF/DL
• Stochastic
GBM
(row/col
sampling)
• Distributions
(Gaussian,
Laplace,
Poisson,
Gamma,
Tweedie)
for
GBM/DL
• Improved
sparse
data
handling
for
DL
• Multi-­‐node
auto-­‐tuning
for
DL
• Multinomial
GLM
• Scalable
Scatter
Plots
for
numeric
and
categorical
data
• Big-­‐Big
Joins
(“distributed
data.table”)
-­‐
in
QA
…and
many
more!

8. Convergence-­‐Based
Early
Stopping
in
H2O
Before:
trains
too
long,
but
at
least
overwrite_with_best_model=true
prevents
overfitting
(returns
the
model
with
lowest
validation
error)
Now:
specify
additional
convergence
criterion:
E.g.
stopping_rounds=5,
stopping_metric=“MSE”,
stopping_tolerance=1e-­‐3,
to
stop
as
soon
as
the
moving
average
(length
5)
of
the
validation
MSE
does
not
improve
by
at
least
0.1%
for
5
consecutive
scoring
events
validation
error
training
error
overwrite_with_best_model=true
training
time
/
epochs
training
time
/
epochsUse
Flow
to
inspect
the
model
Early
stopping
saves
tons
of
time
Best
Model
Deep
Learning
with
Higgs
data

9. What
do
these
stickers
mean?
I have H2O
Installed
I have Python
installed
I have R
installed
I have the H2O
World data
sets
Pick
up
stickers
or
get
install
help
at
the
information
booth