webinar: deep learning with h2o
DESCRIPTION
Note: Make sure to download the slides to get the high-resolution version! Also, you can find the webinar recording here (please also download for better quality): https://www.dropbox.com/s/72qi6wjzi61gs3q/H2ODeepLearningArnoCandel052114.mov Come hear how Deep Learning in H2O is unlocking never before seen performance for prediction! H2O is google-scale open source machine learning engine for R & Big Data. Enterprises can now use all of their data without sampling and build intelligent applications. This live webinar introduces Distributed Deep Learning concepts, implementation and results from recent developments. Real world classification & regression use cases from eBay text dataset, MNIST handwritten digits and Cancer datasets will present the power of this game changing technology.TRANSCRIPT
Deep Learning with H2O
!
H2O.aiScalable In-Memory Machine Learning
!
Webinar, 5/21/14
SriSatish Ambati, CEO and Co-Founder Arno Candel, PhD, Physicst & Hacker
H2O Deep Learning, @ArnoCandel
Outline
Intro & Live Demo (5 mins)
Methods & Implementation (10 mins)
Results & Live Demo (10 mins)
MNIST handwritten digits
text classification
Q & A (10 mins)
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H2O Deep Learning, @ArnoCandel 3
About H20 (aka 0xdata)Pure Java, Apache v2 Open Source Join the www.h2o.ai/community!
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+1 Cyprien Noel for prior work
H2O Deep Learning, @ArnoCandel
Customer Demands for Practical Machine Learning
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Requirements Value
In-Memory Fast (Interactive)
Distributed Big Data (No Sampling)
Open Source Ownership of Methods
API / SDK Extensibility
H2O was developed by 0xdata to meet these requirements
H2O Deep Learning, @ArnoCandel
H2O Integration
H2O
HDFS HDFS HDFS
YARN Hadoop MR
R ScalaJSON Python
Standalone Over YARN On MRv1
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H2O H2O
Java
H2O Deep Learning, @ArnoCandel
H2O Architecture
Distributed In-Memory K-V storeCol. compression
Machine Learning
Algorithms
R EngineNano fast
Scoring Engine
Prediction Engine
Memory manager
e.g. Deep Learning
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MapReduce
H2O Deep Learning, @ArnoCandel
H2O + R = Happy Data Scientist
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Machine Learning on Big Data with R:Data resides on the H2O cluster!
H2O Deep Learning, @ArnoCandel
H2O Deep Learning in Action
Train: 60,000 rows 784 integer columns 10 classes Test: 10,000 rows 784 integer columns 10 classes
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MNIST = Digitized handwritten digits database (Yann LeCun)
Live Demo Build a H2O Deep Learning model on MNIST train/test data
Data: 28x28=784 pixels with (gray-scale) values in 0…255
Yann LeCun: “Yet another advice: don't get fooled by people who claim to have a solution to Artificial General Intelligence. Ask them what error rate they get on MNIST or ImageNet.”
H2O Deep Learning, @ArnoCandel
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.
What is Deep Learning?
Example: Input data(image)
Prediction (who?)
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Facebook's DeepFace (Yann LeCun) recognises faces as well as humans
H2O Deep Learning, @ArnoCandel
Deep Learning is Trending
20132012
Google trends
2011
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Businesses are usingDeep Learning techniques!
Google Brain (Andrew Ng, Jeff Dean & Geoffrey Hinton) !FBI FACE: $1 billion face recognition project !Chinese Search Giant Baidu Hires Man Behind the “Google Brain” (Andrew Ng)
H2O Deep Learning, @ArnoCandel
What is NOT DeepLinear 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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H2O Deep Learning, @ArnoCandel
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) !+ breakthrough algorithms for accuracy (weight initialization, adaptive learning, momentum, dropout, regularization)
!
= Top-notch prediction engine!
Deep Learning in H2O12
H2O Deep Learning, @ArnoCandel
“fully connected” directed graph of neurons
age
income
employment
married
single
Input layerHidden layer 1
Hidden layer 2
Output layer
3x4 4x3 3x2#connections
information flow
input/output neuronhidden neuron
4 3 2#neurons 3
Example Neural Network13
H2O Deep Learning, @ArnoCandel
age
income
employmentyj = 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
single
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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H2O Deep Learning, @ArnoCandel
Mean Square Error = (0.22 + 0.22)/2 “penalize differences per-class” ! Cross-entropy = -log(0.8) “strongly penalize non-1-ness”
Training: Update Weights & Biases
Stochastic Gradient Descent: Update weights and biases via gradient of the error (via back-propagation):
For each training row, we make a prediction and compare with the actual label (supervised learning):
married10.8predicted actual
Objective: minimize prediction error (MSE or cross-entropy)
w <— w - rate * ∂E/∂w
1
15
single00.2
E
wrate
H2O Deep Learning, @ArnoCandel
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 w2w4
w2+w4w1+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 model: weights and biases w
updated model: w*
H2O atomic in-memoryK-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
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2 431
1
1
1
43 2
1 2
1
i
*user can specify the number of total rows per MapReduce iteration
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H2O Deep Learning, @ArnoCandel
“Secret” Sauce to Higher Accuracy
Adaptive learning rate - ADADELTA (Google)Automatically set learning rate for each neuron based on its training history
Grid Search and Checkpointing Run a grid search to scan many hyper-parameters, then continue training the most promising model(s)
RegularizationL1: penalizes non-zero weights L2: penalizes large weightsDropout: randomly ignore certain inputs
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H2O Deep Learning, @ArnoCandel
MNIST: digits classification
Standing world record: Without distortions or convolutions, the best-ever published error rate on test set: 0.83% (Microsoft)
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Time to check in on the demo!
Let’s see how H2O did in the past 10 minutes!
H2O Deep Learning, @ArnoCandel
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
On 4 nodes
H2O Deep Learning, @ArnoCandel
Use Case: Text Classification
Goal: Predict the item from seller’s text description
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Train: 578,361 rows 8,647 cols 467 classes Test: 64,263 rows 8,647 cols 143 classes
“Vintage 18KT gold Rolex 2 Tone in great condition”
Data: Binary word vector 0,0,1,0,0,0,0,0,1,0,0,0,1,…,0
vintagegold condition
Let’s see how H2O does on the ebay dataset!
H2O Deep Learning, @ArnoCandel
Out-Of-The-Box: 11.6% test set error after 10 epochs! Predicts the correct class (out of 143) 88.4% of the time!
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Note 2: No tuning was done(results are for illustration only)
Train: 578,361 rows 8,647 cols 467 classes Test: 64,263 rows 8,647 cols 143 classes
Note 1: H2O columnar-compressed in-memory store only needs 60 MB to store 5 billion values (dense CSV needs 18 GB)
Use Case: Text Classification
H2O Deep Learning, @ArnoCandel
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
H2O Deep Learning, @ArnoCandel
Outlook for H2O Deep Learning23
Convolutional and Pooling Layers for General Image Recognition (ImageNet)
Sparse Auto-Encoders for Dimensionality Reduction and Anomaly Detection
Execution on GPU clusters for even faster training
H2O Deep Learning, @ArnoCandel
H2O Steam: Scoring Platform
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H2O Deep Learning, @ArnoCandel
H2O Steam: More Coming Soon!
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H2O Deep Learning, @ArnoCandel
Key Take-Aways
H2O is a distributed in-memory math platform for enterprise-grade machine learning applications. !
H2O Deep Learning is ready to take your advanced analytics to the next level - Try it on your data! !
Join our Community and Meetups! git clone https://github.com/0xdata/h2o http://docs.0xdata.com www.h2o.ai/community @hexadata
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