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![Sorting Cucumbers
• Classification Problem
• Input: cucumber photos (side, top,
bottom)
• Output: one of nine classes
• Google’s Blog Post [Link]
• YouTube Video [Link]
18](https://image.slidesharecdn.com/20170223metissfdeepwater-170224192432/75/H2O-Deep-Water-Making-Deep-Learning-Accessible-to-Everyone-18-2048.jpg)
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H2O Deep Water - Making Deep Learning Accessible to Everyone
The document provides an overview of H2O.ai and its deep learning platform, Deep Water, which integrates various machine learning libraries such as TensorFlow, MXNet, and Caffe for improved performance and ease of use. It outlines the company's mission to democratize artificial intelligence and data science through open-source tools and detailed architecture for various neural networks. Additionally, it summarizes key aspects of supervised and unsupervised learning methods supported by H2O's platform.
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H2O Deep Water - Making Deep Learning Accessible to Everyone
- 1. H2O Deep Water MakingDeep Learning Accessible to Everyone Jo-fai (Joe) Chow Data Scientist joe@h2o.ai @matlabulous SF Big Data Science at Metis 23rd February, 2017
- 2. About Me • Civil(Water) Engineer • 2010 – 2015 • Consultant (UK) • Utilities • Asset Management • Constrained Optimization • Industrial PhD (UK) • Infrastructure Design Optimization • Machine Learning + Water Engineering • Discovered H2O in 2014 • Data Scientist • From 2015 • Virgin Media (UK) • Domino Data Lab (Silicon Valley) • H2O.ai (Silicon Valley) 2
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- 4. Agenda • About H2O.ai •Company • Machine Learning Platform • Deep Learning Tools • TensorFlow, MXNet, Caffe, H2O • Deep Water • Motivation • Benefits • Interface • Learning Resources • Conclusions 4
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- 6. Company Overview Founded 2011Venture-backed, debuted in 2012 Products • H2O Open Source In-Memory AI Prediction Engine • Sparkling Water • Steam Mission Operationalize Data Science, and provide a platform for users to build beautiful data products Team 70 employees • Distributed Systems Engineers doing Machine Learning • World-class visualization designers Headquarters Mountain View, CA 6
- 7. H2O Machine LearningPlatform 7
- 8. Supervised Learning • GeneralizedLinear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Algorithms Overview Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning 8
- 9. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O ComputeEngine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 9 Flow (Web), R, Python API Java for computation
- 10. 10 H2O Flow (Web)Interface
- 11. 11 H2O Flow (Web)Interface
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- 14. Deep Learning Tools TensorFlow,mxnet, Caffe and H2O Deep Learning 14
- 15. TensorFlow • Open sourcemachine learning framework by Google • Python / C++ API • TensorBoard • Data Flow Graph Visualization • Multi CPU / GPU • v0.8+ distributed machines support • Multi devices support • desktop, server and Android devices • Image, audio and NLP applications • HUGE Community • Support for Spark, Windows … 15 https://github.com/tensorflow/tensorflow
- 16. TensorFlow Wrappers • TFLearn– Simplified interface • keras – TensorFlow + Theano • tensorflow.rb – Ruby wrapper • TensorFlow.jl – Julia wrapper • TensorFlow for R – R wrapper • … and many more! • See: github.com/jtoy/awesome- tensorflow 16
- 17. Sorting Cucumbers • Problem •Sorting cucumbers is a laborious process. • In a Japanese farm, the farmer’s wife can spend up to eight hours a day sorting cucumbers during peak harvesting period. • Solution • Farmer’s son (Makoto Koike) used TensorFlow, Arduino and Raspberry Pi to create an automatic cucumber sorting system. 17
- 18. Sorting Cucumbers • ClassificationProblem • Input: cucumber photos (side, top, bottom) • Output: one of nine classes • Google’s Blog Post [Link] • YouTube Video [Link] 18
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- 22. TensorKart – Self-drivingMario Kart 22 https://github.com/kevinhughes27/TensorKart https://opensourceforu.com/2017/01/tensorflow-brings-self-driving-to-mario-kart/
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- 24. Caffe • Convolution ArchitectureFor Feature Extraction (CAFFE) • Pure C++ / CUDA architecture for deep learning • Command line, Python and MATLAB interface • Model Zoo • Open collection of models 24 https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/
- 25. Supervised Learning • GeneralizedLinear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations H2O Deep Learning Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning 25
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- 27. Both TensorFlow andH2O are widely used 27
- 28. TensorFlow , MXNet,Caffe and H2O democratize the power of deep learning. H2O platform democratizes artificial intelligence & big data science. There are other open source deep learning libraries like Theano and Torch too. Let’s have a party, this will be fun! 28
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- 30. Deep Water Next-Gen DistributedDeep Learning with H2O H2O integrates with existing GPU backends for significant performance gains One Interface - GPU Enabled - Significant Performance Gains Inherits All H2O Properties in Scalability, Ease of Use and Deployment Recurrent Neural Networks enabling natural language processing, sequences, time series, and more Convolutional Neural Networks enabling Image, video, speech recognition Hybrid Neural Network Architectures enabling speech to text translation, image captioning, scene parsing and more Deep Water 30
- 31. Deep Water Architecture Node1 Node N Scala Spark H2O Java Execution Engine TensorFlow/mxnet/Caffe C++ GPU CPU TensorFlow/mxnet/Caffe C++ GPU CPU RPC R/Py/Flow/Scala client REST API Web server H2O Java Execution Engine grpc/MPI/RDMA Scala Spark 31
- 32. 32 Using H2O Flowto train Deep Water Model
- 33. Available Networks inDeep Water • LeNet • AlexNet • VGGNet • Inception (GoogLeNet) • ResNet (Deep Residual Learning) • Build Your Own 33 ResNet
- 34. 34 Choosing different networkstructures
- 35. 35 Choosing different backends (TensorFlow,MXNet, Caffe)
- 36. Unified Interface forTF, MXNet and Caffe 36 Change backend to “mxnet”, “caffe” or “auto” Choosing different network structures
- 37. Easy Stacking withother H2O Models 37 Ensemble of Deep Water, Gradient Boosting Machine & Random Forest models
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- 41. Deep Water H2O+ TensorFlow Demo • H2O + TensorFlow • Dataset – Cat/Dog/Mouse • TensorFlow as GPU backend • Train a LeNet (CNN) model • Interfaces • Python (Jupyter Notebook) • Web (H2O Flow) • Code and Data • github.com/h2oai/deepwater 41
- 42. Data – Cat/Dog/MouseImages 42
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- 46. Deep Water BasicUsage 46
- 47. Unified Interface forTF, MXNet and Caffe 47 Change backend to “mxnet”, “caffe” or “auto” Choosing different network structures
- 48. 48 Using GPU fortraining
- 49.
- 50. Deep Water –Custom Network 50
- 51. 51 Saving the customnetwork structure as a file
- 52. 52 Creating the customnetwork structure with size = 28x28 and channels = 3
- 53. 53 Specifying the custom networkstructure for training
- 54.
- 55. Project “Deep Water” •H2O + TF + MXNet + Caffe • a powerful combination of widely used open source machine learning libraries. • All Goodies from H2O • inherits all H2O properties in scalability, ease of use and deployment. • Unified Interface • allows users to build, stack and deploy deep learning models from different libraries efficiently. 55 • 100% Open Source • the party will get bigger!
- 56. Deep Water –Current Contributors 56
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- 58. • Organizers &Sponsors • Metis & H2O.ai • Code, Slides & Documents • bit.ly/h2o_meetups • bit.ly/h2o_deepwater • docs.h2o.ai • Contact • joe@h2o.ai • @matlabulous • github.com/woobe 58 Thanks! Making Machine Learning Accessible to Everyone Photo credit: Virgin Media
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