Invited talk given at Paypal's data science seminar on April 24 2014. http://docs.0xdata.com/datascience/deeplearning.html - 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. Deep Learning
with H2O
!
H2O.ai
Scalable In-Memory Machine Learning
!
PayPal, San Jose, 4/24/14
Arno Candel

2. Who am I?
PhD in Computational Physics, 2005
from ETH Zurich Switzerland
!
6 years at SLAC - Accelerator Physics Modeling
2 years at Skytree, Inc - Machine Learning
4 months at 0xdata/H2O - Machine Learning
!
10+ years in HPC, C++, MPI, Supercomputing
@ArnoCandel

3. H2O Deep Learning, A. Candel
Outline
Intro
Theory
Implementation
Results
MNIST handwritten digits classification
Live Demo
Prostate cancer classification and age regression
text classification
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4. H2O Deep Learning, A. Candel
Distributed in-memory math platform
➔ GLM, GBM, RF, K-Means, PCA, Deep Learning
Easy to use SDK / API
➔ Java, R, Scala, Python, JSON, Browser-based GUI
!
Businesses can use ALL of their data (w or w/o Hadoop)
➔ Modeling without Sampling
Big Data + Better Algorithms
➔ Better Predictions
H2O Open Source in-memory
Prediction Engine for Big Data
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5. H2O Deep Learning, A. Candel
About H20 (aka 0xdata)
Pure Java, Apache v2 Open Source
Join the www.h2o.ai/community!
5
+1 Cyprien Noel for prior work

6. H2O Deep Learning, A. Candel
H2O w or w/o Hadoop
H2O
H2O H2O
HDFS HDFS HDFS
YARN Hadoop MR
R Java Scala JSON Python
Standalone Over YARN On MRv1
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7. H2O Deep Learning, A. Candel
H2O Architecture
in-memory K-V store
compression
Machine
Learning
Algorithms
R Engine
Nano fast
Scoring Engine
Prediction Engine
memory manager
e.g. Deep Learning
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MapReduce

8. H2O Deep Learning, A. Candel
Wikipedia:
Deep learning is a set of algorithms in machine learning
that attempt to model high-level abstractions in data by
using architectures composed of multiple non-linear
transformations.
!
!
!
!
!
Facebook DeepFace (LeCun): “Almost as good as humans at recognising faces”
!
Google Brain (Andrew Ng, Jeff Dean & Geoffrey Hinton)
!
FBI FACE: $1 billion face recognition project
What is Deep Learning?
Example:
Input data
(facial image)
Prediction
(person’s ID)
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9. H2O Deep Learning, A. Candel
Deep Learning is trending
20132012
Google trends
2011
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10. H2O Deep Learning, A. Candel
Deep Learning History
slides by Yan LeCun (now Facebook)
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Deep Learning wins competitions
AND
makes humans, businesses and
machines (cyborgs!?) smarter

11. H2O Deep Learning, A. Candel
What is NOT Deep
Linear models are not deep
(by definition)
!
Neural nets with 1 hidden layer are not deep
(no feature hierarchy)
!
SVMs and Kernel methods are not deep
(2 layers: kernel + linear)
!
Classification trees are not deep
(operate on original input space)
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12. H2O Deep Learning, A. Candel
1970s multi-layer feed-forward Neural Network
(supervised learning with stochastic gradient descent using back-propagation)
!
+ distributed processing for big data
(H2O in-memory MapReduce paradigm on distributed data)
!
+ multi-threaded speedup
(H2O Fork/Join worker threads update the model asynchronously)
!
+ smart algorithms for accuracy
(weight initialization, adaptive learning, momentum, dropout, regularization)
!
= Top-notch prediction engine!
Deep Learning in H2O
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13. H2O Deep Learning, A. Candel
“fully connected” directed graph of neurons
age
income
employment
married
not married
Input layer
Hidden
layer 1
Hidden
layer 2
Output layer
3x4 4x3 3x2#connections
information flow
input/output neuron
hidden neuron
4 3 2#neurons 3
Example Neural Network
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14. H2O Deep Learning, A. Candel
age
income
employment
yj = tanh(sumi(xi*uij)+bj)
uij
xi
yj
per-class probabilities
sum(pl) = 1
zk = tanh(sumj(yj*vjk)+ck)
vjk
zk
pl
pl = softmax(sumk(zk*wkl)+dl)
wkl
softmax(xk) = exp(xk) / sumk(exp(xk))
“neurons activate each other via weighted sums”
Prediction: Forward Propagation
married
not married
activation function: tanh
alternative:
x -> max(0,x) “rectifier”
pl is a non-linear function of xi:
can approximate ANY function
with enough layers!
bj, ck, dl: bias values
(indep. of inputs)
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15. H2O Deep Learning, A. Candel
age
income
employment
xi
standardize input xi: mean = 0, stddev = 1
!
horizontalize categorical variables, e.g.
{full-time, part-time, none, self-employed}
->
{0,1,0} = part-time, {0,0,0} = self-employed
Poor man’s initialization: random weights
!
Better: Uniform distribution in
+/- sqrt(6/(#units + #units_previous_layer))
Data preparation & Initialization
Neural Networks are sensitive to numerical noise,
operate best in the linear regime (not saturated)
married
not married
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16. H2O Deep Learning, A. Candel
Mean Square Error = (0.22 + 0.22)/2 “penalize differences per-class”
!
Cross-entropy = -log(0.8) “strongly penalize non-1-ness”
Stochastic Gradient Descent
SGD: improve weights and biases for EACH training row
married
not married
For each training row, we make a prediction and compare
with the actual label (supervised training):
1
0
0.8
0.2
predicted actual
Objective: minimize prediction error (MSE or cross-entropy)
w <— w - rate * ∂E/∂w
1
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17. H2O Deep Learning, A. Candel
Backward Propagation
!
∂E/∂wi = ∂E/∂y * ∂y/∂net * ∂net/∂wi
= ∂(error(y))/∂y * ∂(activation(net))/∂net * xi
Backprop: Compute ∂E/∂wi via chain rule going backwards
wi
net = sumi(wi*xi) + b
xi
E = error(y)
y = activation(net)
How to compute ∂E/∂wi for wi <— wi - rate * ∂E/∂wi ?
Naive: For every i, evaluate E twice at (w1,…,wi±∆,…,wN)… Slow!
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18. H2O Deep Learning, A. Candel
H2O Deep Learning Architecture
K-V
K-V
HTTPD
HTTPD
nodes/JVMs: sync
threads: async
communication
w
w w
w w w w
w1
w3 w2
w4
w2+w4
w1+w3
w* = (w1+w2+w3+w4)/4
map:
each node trains a
copy of the weights
and biases with
(some* or all of) its
local data with
asynchronous F/J
threads
initial weights and biases w
updated weights and biases w*
H2O atomic
in-memory
K-V store
reduce:
model averaging:
average weights and
biases from all nodes,
speedup is at least
#nodes/log(#rows)
arxiv:1209.4129v3
Keep iterating over the data (“epochs”), score from time to time
Query & display
the model via
JSON, WWW
2
2 431
1
1
1
4
3 2
1 2
1
i
*user can specify the number of total rows per MapReduce iteration
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19. H2O Deep Learning, A. Candel
“Secret” Sauce to Higher Accuracy
Momentum training:
keep changing weights and biases (even if there’s no error)
“find other local minima, and go faster along valleys”
Adaptive learning rate - ADADELTA (Google):
automatically set learning rate for each neuron based on its
training history, combines annealing and momentum features
Learning rate annealing:
rate r = r0 / (1+ß*N), N = training samples
“dig deeper into local minimum”
Grid Search and Checkpointing:
Run a grid search over multiple hyper-parameters,
then continue training the best model
L1/L2/Dropout/max_w2 regularization:
L1: penalizes non-zero weights, L2: penalizes large weights
Dropout: randomly ignore certain inputs “train exp. many models at once”
max_w2: Scale down all incoming weights if their squared sum > max_w2
“regularization avoids overtraining and improves generalization error”
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20. H2O Deep Learning, A. Candel
Adaptive Learning Rate
!
Compute moving average of ∆wi
2 at time t for window length rho:
!
E[∆wi
2]t = rho * E[∆wi
2]t-1 + (1-rho) * ∆wi
2
!
Compute RMS of ∆wi at time t with smoothing epsilon:
!
RMS[∆wi]t = sqrt( E[∆wi
2]t + epsilon )
Adaptive annealing / progress:
Gradient-dependent learning rate,
moving window prevents “freezing”
(unlike ADAGRAD: no window)
Adaptive acceleration / momentum:
accumulate previous weight updates,
but over a window of time
RMS[∆wi]t-1
RMS[∂E/∂wi]t
rate(wi, t) =
Do the same for ∂E/∂wi, then
obtain per-weight learning rate:
cf. ADADELTA paper

21. H2O Deep Learning, A. Candel
Dropout Training
Training:
For each hidden neuron, for each training sample, for each iteration,
ignore (zero out) a different random fraction p of input activations.
!
age
income
employment
married
not married
X
X
X
Testing:
Use all activations, but reduce them by a factor p
(to “simulate” the missing activations during training).
cf. Geoff Hinton's paper

22. H2O Deep Learning, A. Candel
MNIST: digits classification
Train: 60,000 rows 784 integer columns 10 classes
Test: 10,000 rows 784 integer columns 10 classes
MNIST: Digitized handwritten digits database (Yann LeCun)
Data: 28x28=784 pixels with values in 0…255 (gray-scale)
One of the most popular multi-class classification problems
Without distortions or convolutions
(which help), the best-ever published
error rate on test set: 0.83% (Microsoft)
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23. H2O Deep Learning, A. Candel
Frequent errors: confuse 2/7 and 4/9
H2O Deep Learning on MNIST:
0.87% test set error (so far)
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test set error: 1.5% after 10 mins
1.0% after 1.5 hours
0.87% after 4 hours
World-class
results!
No pre-training
No distortions
No convolutions
No unsupervised
training
Running on 4
nodes with 16
cores each

24. H2O Deep Learning, A. Candel
Parallel Scalability
(for 64 epochs on MNIST, with “0.87%” parameters)
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Speedup
0.00
10.00
20.00
30.00
40.00
1 2 4 8 16 32 63
H2O Nodes
(4 cores per node, 1 epoch per node per MapReduce)
2.7 mins
Training Time
0
25
50
75
100
1 2 4 8 16 32 63
H2O Nodes
in minutes

25. H2O Deep Learning, A. Candel
Prostate Cancer Dataset
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26. H2O Deep Learning, A. Candel
Live Demo: Cancer Prediction
Interactive ROC
curve with real-
time updates
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27. H2O Deep Learning, A. Candel
Live Demo: Cancer Prediction
0% training error
with only 322
model parameters
in seconds!
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28. H2O Deep Learning, A. Candel
H2O Deep Learning with Scala
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Predict CAPSULE: Variable 1

29. H2O Deep Learning, A. Candel
H2O Deep Learning with Scala
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30. H2O Deep Learning, A. Candel
Live Demo: Grid Search Regression
Doing a grid search to find good hyper-parameters
to predict AGE from other 7 features
Then continue training the best model
5 hidden 50 tanh layers, rho=0.99,
epsilon = 1e-10, normal distribution scale=1
MSE = 0.5 for test
set ages in 44…79
Regression:
1 linear output
neuron
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31. H2O Deep Learning, A. Candel
Live Demo: ebay Text Classification
Users enter a description when selling an item
Task: Predict the type of item from the words used
Data prep: Binary word vector 0,0,1,0,0,0,0,0,1,0,0,0,1,…,0
H2O parses SVMLight sparse format: label 3:1 9:1 13:1 …
!
“Small” sample dataset on jewelry and watches:
Train: 578,361 rows 8,647 cols 467 classes
Test: 64,263 rows 8,647 cols 143 classes
!
H2O compressed columnar in-memory store:
Only needs 60MB to store 5 billion entries (never inflated)
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32. H2O Deep Learning, A. Candel
Live Demo: ebay Text Classification
No tuning (results for illustration only):
11.6% test set error (<4% for top-5) after only 10 epochs!
Train: 578,361 rows 8,647 cols 467 classes
Test: 64,263 rows 8,647 cols 143 classes
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33. H2O Deep Learning, A. Candel
Tips for H2O Deep Learning
!
General:
More layers for more complex functions (exp. more non-linearity)
More neurons per layer to detect finer structure in data (“memorizing”)
Add some regularization for less overfitting (smaller validation error)
Do a grid search to get a feel for convergence, then continue training.
Try Tanh first, then Rectifier, try max_w2 = 50 and/or L1=1e-5.
Try Dropout (input: 20%, hidden: 50%) with test/validation set after
finding good parameters for convergence on training set.
Distributed: More training samples per iteration: faster, but less accuracy?
With ADADELTA: Try epsilon = 1e-4,1e-6,1e-8,1e-10, rho = 0.9,0.95,0.99
Without ADADELTA: Try rate = 1e-4…1e-2, rate_annealing = 1e-5…1e-8,
momentum_start = 0.5, momentum_stable = 0.99,
momentum_ramp = 1/rate_annealing
Try balance_classes = true for imbalanced classes.
Use force_load_balance and replicate_training_data for small datasets.
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34. H2O Deep Learning, A. Candel
Summary
H2O is a distributed in-memory math platform that
allows fast prototyping in Java, R, Scala and Python.
!
H2o enables the development of enterprise-quality
blazingly fast machine learning applications.
!
H2O Deep Learning is distributed, easy to use, and
early results compete with the world’s best.
!
Try it yourself and join our next meetup!
git clone https://github.com/0xdata/h2o
http://docs.0xdata.com www.h2o.ai/community
follow us on Twitter: @hexadata
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