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Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)

In this talk, we take Deep Learning to task with real world data puzzles to solve.

Data:

- Higgs binary classification dataset (10M rows, 29 cols)

- MNIST 10-class dataset

- Weather categorical dataset

- eBay text classification dataset (8500 cols, 500k rows, 467 classes)

- ECG heartbeat anomaly detection

- 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

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- 1. Deep Learning through Examples Arno Candel ! 0xdata, H2O.ai Scalable In-Memory Machine Learning ! Silicon Valley Big Data Science Meetup, Palo Alto, 9/3/14 !
- 2. Who am I? @ArnoCandel PhD in Computational Physics, 2005 from ETH Zurich Switzerland ! 6 years at SLAC - Accelerator Physics Modeling 2 years at Skytree, Inc - Machine Learning 9 months at 0xdata/H2O - Machine Learning ! 15 years in HPC/Supercomputing/Modeling ! Named “2014 Big Data All-Star” by Fortune Magazine !
- 3. H2O Deep Learning, @ArnoCandel Outline Intro & Live Demo (10 mins) Methods & Implementation (20 mins) Results & Live Demos (25 mins) Higgs boson detection MNIST handwritten digits text classification Q & A (5 mins) 3
- 4. H2O Deep Learning, @ArnoCandel About H20 (aka 0xdata) Java, Apache v2 Open Source Join the www.h2o.ai/community! #1 Java Machine Learning in Github 4
- 5. H2O Deep Learning, @ArnoCandel Customer Demands for Practical Machine Learning 5 Requirements Value In-Memory Fast (Interactive) Distributed Big Data (No Sampling) Open Source Ownership of Methods API / SDK Extensibility H2O was developed by 0xdata from scratch to meet these requirements
- 6. H2O Deep Learning, @ArnoCandel H2O Integration H2O R JSON Scala Python YARN Hadoop MR HDFS HDFS HDFS Standalone Over YARN On MRv1 6 H2O H2O Java
- 7. H2O Deep Learning, @ArnoCandel H2O Architecture Prediction Engine Distributed In-Memory K-V store Col. compression Machine Learning Algorithms R Engine Nano fast Scoring Engine Memory manager e.g. Deep Learning 7 MapReduce
- 8. H2O Deep Learning, @ArnoCandel H2O - The Killer App on Spark 8 http://databricks.com/blog/2014/06/30/ sparkling-water-h20-spark.html
- 9. H2O Deep Learning, @ArnoCandel H2O DeepLearning on Spark 9 // Test if we can correctly learn A, B where Y = logistic(A + B*X) test("deep learning log regression") { val nPoints = 10000 val A = 2.0 val B = -1.5 ! // Generate testing data val trainData = DeepLearningSuite.generateLogisticInput(A, B, nPoints, 42) // Create RDD from testing data val trainRDD = sc.parallelize(trainData, 2) trainRDD.cache() ! import H2OContext._ // Create H2O data frame (will be implicit in the future) val trainH2ORDD = toDataFrame(sc, trainRDD) // Create a H2O DeepLearning model val dlParams = new DeepLearningParameters() dlParams.source = trainH2ORDD dlParams.response = trainH2ORDD.lastVec() dlParams.classification = true val dl = new DeepLearning(dlParams) val dlModel = dl.train().get() ! // Score validation data val validationData = DeepLearningSuite.generateLogisticInput(A, B, nPoints, 17) val validationRDD = sc.parallelize(validationData, 2) val validationH2ORDD = toDataFrame(sc, validationRDD) val predictionH2OFrame = new DataFrame(dlModel.score(validationH2ORDD))('predict) val predictionRDD = toRDD[DoubleHolder](sc, predictionH2OFrame) // will be implicit in the future // Validate prediction validatePrediction( predictionRDD.collect().map (_.predict.getOrElse(Double.NaN)), validationData) } Brand-Sparkling-New Sneak Preview!
- 10. H2O Deep Learning, @ArnoCandel 10 H2O R CRAN package John Chambers (creator of the S language, R-core member) names H2O R API in top three promising R projects
- 11. H2O Deep Learning, @ArnoCandel H2O + R = Happy Data Scientist 11 Machine Learning on Big Data with R: Data resides on the H2O cluster!
- 12. H2O Deep Learning, @ArnoCandel 12 Higgs Particle Discovery Large Hadron Collider: Largest experiment of mankind! $13+ billion, 16.8 miles long, 120 MegaWatts, -456F, 1PB/day, etc. Higgs boson discovery (July ’12) led to 2013 Nobel prize! Higgs vs Background http://arxiv.org/pdf/1402.4735v2.pdf Images courtesy CERN / LHC Machine Learning Meets Physics Or rather: Back to the roots (WWW was invented at CERN in ’89…)
- 13. H2O Deep Learning, @ArnoCandel 13 Higgs: Binary Classification Problem Current methods of choice for physicists: - Boosted Decision Trees - Neural networks with 1 hidden layer BUT: Must first add derived high-level features (physics formulae) HIGGS UCI Dataset: 21 low-level features AND 7 high-level derived features Train: 10M rows, Test: 500k rows Metric: AUC = Area under the ROC curve (range: 0.5…1, higher is better) Algorithm low-level H2O AUC all features H2O AUC Generalized Linear Model 0.596 0.684 add derived Random Forest 0.764 0.840 features Gradient Boosted Trees 0.753 0.839 Neural Net 1 hidden layer 0.760 0.830
- 14. H2O Deep Learning, @ArnoCandel 14 Higgs: Can Deep Learning Do Better? Algorithm low-level H2O AUC all features H2O AUC Generalized Linear Model 0.596 0.684 Random Forest 0.764 0.840 Gradient Boosted Trees 0.753 0.839 Neural Net 1 hidden layer 0.760 0.830 Deep Learning ? ? <Your guess goes here> reference paper results: baseline 0.733 Let’s build a H2O Deep Learning model and find out! (That was my last weekend)
- 15. H2O Deep Learning, @ArnoCandel What is Deep Learning? 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. Example: Input data (image) Prediction (who is it?) 15 Facebook's DeepFace (Yann LeCun) recognises faces as well as humans
- 16. H2O Deep Learning, @ArnoCandel What is NOT Deep Linear models are not deep (by definition) ! Neural nets with 1 hidden layer are not deep (only 1 layer - no feature hierarchy) ! SVMs and Kernel methods are not deep (2 layers: kernel + linear) ! Classification trees are not deep (operate on original input space, no new features generated) 16
- 17. H2O Deep Learning, @ArnoCandel Deep Learning is Trending Google trends 2009 2011 2013 17 Businesses are using Deep 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)
- 18. H2O Deep Learning, @ArnoCandel Deep Learning History slides by Yan LeCun (now Facebook) 18 Deep Learning wins competitions AND makes humans, businesses and machines (cyborgs!?) smarter
- 19. H2O Deep Learning, @ArnoCandel Deep Learning in H2O 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 rate, momentum, dropout regularization, l1/L2 regularization, grid search, checkpointing, auto-tuning, model averaging) ! = Top-notch prediction engine! 19
- 20. H2O Deep Learning, @ArnoCandel Example Neural Network “fully connected” directed graph of neurons age income employment input/output neuron hidden neuron married single Input layer Hidden layer 1 Hidden layer 2 Output layer #connections 3x4 4x3 3x2 information flow #neurons 3 4 3 2 20
- 21. H2O Deep Learning, @ArnoCandel Prediction: Forward Propagation “neurons activate each other via weighted sums” age income employment uij vjk zk pl yj = tanh(sumi(xi*uij)+bj) xi yj 21 married per-class probabilities sum(pl) = 1 wkl zk = tanh(sumj(yj*vjk)+ck) single pl = softmax(sumk(zk*wkl)+dl) softmax(xk) = exp(xk) / sumk(exp(xk)) 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)
- 22. H2O Deep Learning, @ArnoCandel Data preparation & Initialization Neural Networks are sensitive to numerical noise, operate best in the linear regime (not saturated) age income employment xi Automatic standardization of data 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 married single wkl Automatic initialization of weights ! 22 Poor man’s initialization: random weights wkl ! Default (better): Uniform distribution in +/- sqrt(6/(#units + #units_previous_layer))
- 23. H2O Deep Learning, @ArnoCandel Training: Update Weights & Biases For each training row, we make a prediction and compare with the actual label (supervised learning): predicted actual 0.8 1 married Objective: minimize prediction error (MSE or cross-entropy) Mean Square Error = (0.22 + 0.22)/2 “penalize differences per-class” ! Cross-entropy = -log(0.8) “strongly penalize non-1-ness” 1 Stochastic Gradient Descent: Update weights and biases via gradient of the error (via back-propagation): w <— w - rate * ∂E/∂w 23 0.2 0 single E w rate
- 24. H2O Deep Learning, @ArnoCandel Backward Propagation How to compute ∂E/∂wi for wi <— wi - rate * ∂E/∂wi ? Naive: For every i, evaluate E twice at (w1,…,wi±Δ,…,wN)… Slow! Backprop: Compute ∂E/∂wi via chain rule going backwards xi ! net = sumi(wi*xi) + b wi y = activation(net) E = error(y) ∂E/∂wi = ∂E/∂y * ∂y/∂net * ∂net/∂wi = ∂(error(y))/∂y * ∂(activation(net))/∂net * xi 24
- 25. H2O Deep Learning, @ArnoCandel H2O Deep Learning Architecture K-V HTTPD nodes/JVMs: sync threads: async communication K-V HTTPD w 1 w w 2 1 w w w w 1 3 2 4 w1 w3 w2 w4 3 2 w w2+w4 1+w3 4 1 2 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 1 1 updated model: 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 i Query & display the model via JSON, WWW Keep iterating over the data (“epochs”), score from time to time *auto-tuned (default) or user-specified number of points per MapReduce iteration 25
- 26. H2O Deep Learning, @ArnoCandel Adaptive learning rate - ADADELTA (Google) Automatically set learning rate for each neuron based on its training history Regularization L1: penalizes non-zero weights L2: penalizes large weights Dropout: randomly ignore certain inputs Grid Search and Checkpointing Run a grid search to scan many hyper-parameters, then continue training the most promising model(s) 26 “Secret” Sauce to Higher Accuracy
- 27. H2O Deep Learning, @ArnoCandel Detail: Adaptive Learning Rate ! Compute moving average of Δwi2 at time t for window length rho: ! E[Δwi2]t = rho * E[Δwi2]t-1 + (1-rho) * Δwi2 ! Compute RMS of Δwi at time t with smoothing epsilon: ! RMS[Δwi]t = sqrt( E[Δwi2]t + epsilon ) Adaptive acceleration / momentum: accumulate previous weight updates, but over a window of time Adaptive annealing / progress: Gradient-dependent learning rate, moving window prevents “freezing” (unlike ADAGRAD: no window) Do the same for ∂E/∂wi, then obtain per-weight learning rate: RMS[Δwi]t-1 RMS[∂E/∂wi]t rate(wi, t) = cf. ADADELTA paper 27
- 28. H2O Deep Learning, @ArnoCandel Detail: Dropout Regularization 28 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 single 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
- 29. H2O Deep Learning, @ArnoCandel MNIST: digits classification MNIST = Digitized handwritten digits database (Yann LeCun) 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.” Data: 28x28=784 pixels with (gray-scale) values in 0…255 Standing world record: Without distortions or convolutions, the best-ever published error rate on test set: 0.83% (Microsoft) 29 Train: 60,000 rows 784 integer columns 10 classes Test: 10,000 rows 784 integer columns 10 classes Let’s see how H2O does on the MNIST dataset!
- 30. H2O Deep Learning, @ArnoCandel H2O Deep Learning on MNIST: 0.87% test set error (so far) Frequent errors: confuse 2/7 and 4/9 30 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
- 31. H2O Deep Learning, A. Candel Weather Dataset 31 Predict “RainTomorrow” from Temperature, Humidity, Wind, Pressure, etc.
- 32. H2O Deep Learning, A. Candel Live Demo: Weather Prediction 5-fold cross validation Interactive ROC curve with real-time updates 32 3 hidden Rectifier layers, Dropout, L1-penalty 12.7% 5-fold cross-validation error is at least as good as GBM/RF/GLM models
- 33. H2O Deep Learning, @ArnoCandel Live Demo: Grid Search How did I find those parameters? Grid Search! (works for multiple hyper parameters at once) 33 Then continue training the best model
- 34. H2O Deep Learning, @ArnoCandel Text Classification Goal: Predict the item from seller’s text description 34 “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 gold vintage condition Train: 578,361 rows 8,647 cols 467 classes Test: 64,263 rows 8,647 cols 143 classes Let’s see how H2O does on the ebay dataset!
- 35. H2O Deep Learning, @ArnoCandel 35 Text Classification Train: 578,361 rows 8,647 cols 467 classes Test: 64,263 rows 8,647 cols 143 classes Out-Of-The-Box: 11.6% test set error after 10 epochs! Predicts the correct class (out of 143) 88.4% of the time! Note 1: H2O columnar-compressed in-memory store only needs 60 MB to store 5 billion values (dense CSV needs 18 GB) Note 2: No tuning was done (results are for illustration only)
- 36. H2O Deep Learning, @ArnoCandel Parallel Scalability (for 64 epochs on MNIST, with “0.87%” parameters) 36 Speedup 40.00 30.00 20.00 10.00 0.00 1 2 4 8 16 32 63 H2O Nodes Training Time 2.7 mins 100 75 50 25 0 in minutes 1 2 4 8 16 32 63 H2O Nodes (4 cores per node, 1 epoch per node per MapReduce)
- 37. H2O Deep Learning, @ArnoCandel Deep Learning Auto-Encoders for Anomaly Detection 37 Toy example: Find anomaly in ECG heart beat data. First, train a model on what’s “normal”: 20 time-series samples of 210 data points each Deep Auto-Encoder: Learn low-dimensional non-linear “structure” of the data that allows to reconstruct the orig. data Also for categorical data!
- 38. H2O Deep Learning, @ArnoCandel 38 Deep Learning Auto-Encoders for Test set with anomaly Test set prediction is reconstruction, looks “normal” Found anomaly! large reconstruction error Model of what’s “normal” + => Anomaly Detection
- 39. H2O Deep Learning, @ArnoCandel 39 H2O brings Deep Learning to R R Vignette with example R scripts http://0xdata.com/h2o/algorithms/ All parameters are available from R…
- 40. H2O Deep Learning, @ArnoCandel POJO Model Export for Production Scoring 40 Plain old Java code is auto-generated to take your H2O Deep Learning models into production!
- 41. H2O Deep Learning, @ArnoCandel 41 Higgs Particle Discovery with H2O How well did H2O Deep Learning do? <Your guess goes here> reference paper results Any guesses for AUC on low-level features? AUC=0.76 was the best for RF/GBM/NN Let’s see how H2O did in the past 30 minutes!
- 42. H2O Deep Learning, @ArnoCandel H2O Steam: Scoring Platform 42 http://server:port/steam/index.html Higgs Dataset Demo on 10-node cluster Let’s score all our H2O models and compare them! Live Demo
- 43. H2O Deep Learning, @ArnoCandel 43 Scoring Higgs Models in H2O Steam Live Demo on 10-node cluster: <10 minutes runtime for all algos! Better than LHC baseline of AUC=0.73!
- 44. H2O Deep Learning, @ArnoCandel 44 Higgs Particle Detection with H2O HIGGS UCI Dataset: 21 low-level features AND 7 high-level derived features Train: 10M rows, Test: 500k rows Algorithm *Nature paper: http://arxiv.org/pdf/1402.4735v2.pdf Paper’s l-l AUC low-level H2O AUC all features H2O AUC Parameters (not heavily tuned), H2O running on 10 nodes Generalized Linear Model - 0.596 0.684 default, binomial Random Forest - 0.764 0.840 50 trees, max depth 50 Gradient Boosted Trees 0.73 0.753 0.839 50 trees, max depth 15 Neural Net 1 layer 0.733 0.760 0.830 1x300 Rectifier, 100 epochs Deep Learning 3 hidden layers 0.836 0.850 - 3x1000 Rectifier, L2=1e-5, 40 epochs Deep Learning 4 hidden layers 0.868 0.869 - 4x500 Rectifier, L1=L2=1e-5, 300 epochs Deep Learning 6 hidden layers 0.880 running - 6x500 Rectifier, L1=L2=1e-5 Deep Learning on low-level features alone beats everything else! H2O prelim. results compare well with paper’s results* (TMVA & Theano)
- 45. H2O Deep Learning, @ArnoCandel 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 (lower validation set error). Specifically: Do a grid search to get a feel for convergence, then continue training. Try Tanh/Rectifier, try max_w2=10…50, L1=1e-5..1e-3 and/or L2=1e-5…1e-3 Try Dropout (input: up to 20%, hidden: up to 50%) with test/validation set. Input dropout is recommended for noisy high-dimensional input. 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-9, momentum_start = 0.5…0.9, momentum_stable = 0.99, momentum_ramp = 1/rate_annealing. Try balance_classes = true for datasets with large class imbalance. Enable force_load_balance for small datasets. Enable replicate_training_data if each node can h0ld all the data. 45
- 46. H2O Deep Learning, @ArnoCandel Extensions for H2O Deep Learning 46 - Vision: Convolutional & Pooling Layers PUB-644 - Anomaly Detection PUB-806 - Pre-Training: Stacked Auto-Encoders PUB-1014 - Faster Training: GPGPU support PUB-1013 - Language/Sequences: Recurrent Neural Networks - Benchmark vs other Deep Learning packages - Investigate other optimization algorithms Contribute to H2O! Add your own JIRA tickets!
- 47. H2O Deep Learning, @ArnoCandel Key Take-Aways H2O is a distributed in-memory data science platform. It was designed for high-performance machine learning applications on big data. ! H2O Deep Learning is ready to take your advanced analytics to the next level - Try it on your data! ! Join our Community and Meetups! https://github.com/h2oai http://docs.h2o.ai www.h2o.ai/community @h2oai 47 Thank you!

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