Deep Learning Applications in Cycling, Healthcare, and Particle Physics
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Alex Tellez's slides on Deep Learning Applications, including using auto-encoders, finding better Bordeaux wine, and fighting crime in Chicago, from the 3/11/15 Meetup at H2O.ai HQ and the 3/12/15 Meetup at Mills College. - 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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Deep Learning Applications in Cycling, Healthcare, and Particle Physics
- 1. DEEP LEARNING APPLICATIONS Mills College, 3/12/2015 AlexTellez - alex@h2o.ai
- 2. H2O - MORETHAN WATER What is H2O? (water, duh!) It is ALSO an open-source, parallel processing engine for machine learning. What makes H2O different? Cutting-edge algorithms + parallel architecture + ease-of-use = Happy Data Scientists / Analysts
- 3. TEAM @ H2O.AI 16,000 commits H2O World Conference 2014
- 4. COMMUNITY REACH 120 meetups in 2014 11,000 installations 2,000 corporations First Friday Hack-A-Thons
- 5. TRY IT! Don’t take my word for it…www.h2o.ai Simple Instructions 1. CD to Download Location 2. unzip h2o file 3. java -jar h2o.jar 4. Point browser to: localhost:54321 GUI R
- 6. SUPERVISED LEARNING Deep Learning Applications on Labeled Data
- 7. SUPERVISED LEARNING What is it? Examples of supervised learning tasks: 1. ClassificationTasks - Benign / Malignant tumor 2. RegressionTasks - Predicting future stock market prices 3. Image Recognition - Highlighting faces in pictures Methods that infer a function from labeled training data. Key task: Predicting ________ . (Insert your task here)
- 8. SUPERVISED ALGORITHMS Ensembles Deep Neural Networks • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Cox Proportional Hazards Models • Naïve Bayes • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Statistical Analysis VERY HOT subject area & our topic today!
- 9. WHY NEURAL NETS? Linear Classification Non-Linear Classification Error
- 10. NEURAL NETS + H20 Inputs Outputs Hidden Features Neurons activate each other via weighted sums y1 y2 x1 x2 x3 x3 h1 h2 h3 Activation Functions H2O Supports: Tanh Rectifier Maxout
- 11. FINDINGTHE HIGGS-BOSON Task: Can we identify the Higgs-Boson particle vs. background noise using ‘low-level’ machine generated data? Live Demo! CERN Lab
- 12. FIGHTING CRIME IN CHICAGO Spark + H2O
- 13. OPEN CITY, OPEN DATA “…my kind of town” - F. Sinatra ~4.6 Million rows of crimes from 2001, updated weekly* External data source considerations??? Weather Data ?U.S. Census Data ? Crime Data
- 14. ML WORKFLOW 1. Collect datasets (Crime + Weather + Census) 2. Do some feature extraction (e.g. dates, times) 3. Join Crime data Weather Data Census Data 4. Build deep learning model to predict arrest / no arrest made GOAL: For a given crime, predict if an arrest is more / less likely to be made!
- 15. SPARK SQL + H2O RDD 3 table join using Spark SQL Convert joined table to H2O RDD
- 16. H2O DEEP LEARNING Can do grid search over many parameters!
- 17. HOW’D WE DO? nice! ~ 10 mins
- 18. MODEL BUILDING +TUNING DReD Net = Deep Rectifier w/ Dropout Neural Net Arrest Inputs X X X X Epochs, hidden layers, regularization
- 19. UNSUPERVISED LEARNING Deep Learning Applications on Non-Labeled Data
- 20. UNSUPERVISED LEARNING What is it? Examples of unsupervised learning tasks: 1. Clustering - Discovering customer segments 2.Topic Extraction - What topics are people tweeting about? 3. Information Retrieval - IBM Watson: Question + Answer Methods to understand the general structure of input data where no predictions is needed. 4.Anomaly Detection - Detecting irregular heart-beats NO CURATION NEEDED!
- 21. UNSUPERVISED ALGORITHMS Dimensionality Reduction Anomaly Detection • K-means: Partitions observations into k clusters/groups of the same spatial size • Principal Component Analysis: Linearly transforms correlated variables to independent components • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning Clustering
- 22. AUTOENCODER + H2O Input Output Hidden Features Information Flow x1 x2 x3 x4 x1 x2 x3 x4 Dogs, Dogs and Dogs
- 23. ANOMALY DETECTION OFVINTAGE YEAR BORDEAUX WINE
- 24. BORDEAUX WINE Largest wine-growing region in France + 700 Million bottles of wine produced / year ! Some years better than others: Great ($$$) vs.Typical ($) Last Great years: 2010, 2009, 2005, 2000
- 25. GREATVS.TYPICALVINTAGE? Question: Can we study weather patterns in Bordeaux leading up to harvest to identify ‘anomalous’ weather years >> correlates to Great ($$$) vs.Typical ($)Vintage? The Bordeaux Dataset (1952 - 2014 Yearly) Amount of Winter Rain (Oct > Apr of harvest year) Average Summer Temp (Apr > Sept of harvest year) Rain during Harvest (Aug > Sept) Years since last Great Vintage
- 26. AUTOENCODER + ANOMALY DETECTION ML Workflow: 1)Train autoencoder to learn ‘typical’ vintage weather pattern 2) Append ‘great’ vintage year weather data to original dataset 3) IF great vintage year weather data does NOT match learned weather pattern, autoencoder will produce high reconstruction error (MSE) ‘en primeur of en primeur’ - Can we use weather patterns to identify anomalous years >> indicates great vintage quality? Goal:
- 27. RESULTS (MSE > 0.10) Mean Square Error 1961V 2009V 2005V 2000V 1990V 1989V 1982V 2010V
- 28. 2014 BORDEAUX?? Mean Square Error 2014 ?2013
- 29. DEEP AUTOENCODERS + K- MEANS EXAMPLE Help cyclists with their health related questions!
- 30. CYCLING + __________ Problem: New and Experienced Cyclists have questions about cycling + ______ (given topic). Let’s build a question + answer system to help! ML Workflow: 1) Scrape thousands of article titles from internet about cycling / cycling tips / cycling health, etc from various sources. 2) Build Bag-of-Words Dataset on article titles corpus 3) Reduce # of dimensions via deep autoencoder 4) Extract ‘last layer’ of deep features and cluster using k-means 5) Inspect Results!
- 31. BAG-OF-WORDS Build dataset of cycling-related articles from various sources: The Basics of Exercise Nutrition 0 , 0 , 0 , 0 , 1, 1, 0 , 0 , 1, 0 , 0 …, 0 basics exercise nutrition lower case remove ‘stopwords’ remove punctuation Article Title [ ]
- 32. DIMENSIONALITY REDUCTION Use deep autoencoder to reduce # features (~2,700 words!) 2,700Words 500hiddenfeatures 250H.F. 125H.F. 50 125H.F. 250H.F. 500hiddenfeatures 2,700Words Decoder Encoder The Basics of Exercise Nutrition
- 33. K-MEANS CLUSTERING For each article: Extract ‘last’ layer of autoencoder (50 deep features) The Basics of Exercise Nutrition 50 ‘deep features’ The Basics of Exercise Nutrition -‐0.09330833 0.167881429 -‐0.234307408 0.247723639 -‐0.067700267 -‐0.094107866 DF1 DF2 DF3 DF4 DF5 DF6 K-Means Clustering Inputs: Extracted 50 deep features for each cycling-related article K = 50 clusters after grid-search of values
- 34. RESULT: CYCLING + A.I. Now we inspect the clusters! Test Article Title: Fluid & Carbohydrate Ingestion Improve Performance During 1Hour of Intense Exercise Result: Clustered w/ 17 other titles (out of ~5,700) Top 5 similar titles within cluster: Caffeine ingestion does not alter performance during a 100-km cycling time-trial performance Immuno-endocrine response to cycling following ingestion of caffeine and carbohydrate Metabolism and performance following carbohydrate ingestion late in exercise Increases in cycling performance in response to caffeine ingestion are repeatable Fluid ingestion does not influence intense 1-h exercise performance in a mild environment
- 35. HOWTO GET FASTER? Test Article Title: Muscle Coordination is Key to Power Output & Mechanical Efficiency of Limb Movements Result: Clustered w/ 29 other titles (out of ~5,700) Top 5 similar titles within cluster: Muscle fibre type efficiency and mechanical optima affect freely chosen pedal rate during cycling. Standard mechanical energy analyses do not correlate with muscle work in cycling. The influence of body position on leg kinematics and muscle recruitment during cycling. Influence of repeated sprint training on pulmonary O2 uptake and muscle deoxygenation kinetics in humans Influence of pedaling rate on muscle mechanical energy in low power recumbent pedaling using forward dynamic simulations
- 36. WHAT’S NEXT?? Build smarter apps!! alex@h2o.ai github.com/h2oai Hack with us!!
- 37. HIGGS-BOSON PARTICLE How did our Deep Neural Net do?? BEST Low-Level AUC: 0.73