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Deploying Enterprise Deep Learning Masterclass Preview - Enterprise Deep Learning
This document summarizes Sam Putnam's presentation on deploying enterprise deep learning. It discusses how deep learning was used to analyze housing price data and predict prices. A neural network model was built using TensorFlow that improved on previous linear regression and gradient boosted decision tree models. The presentation provides an overview of deep learning concepts like neural networks, activation functions, and model architectures for different data types. It emphasizes real-world considerations for developing and deploying deep learning models in a production setting.
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Deploying Enterprise Deep Learning Masterclass Preview - Enterprise Deep Learning
- 1. Deploying Enterprise Deep Learning SamPutnam, Enterprise Deep Learning, LLC July 27, 2017 Day 2
- 2. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Deep Learning Made This Video https://www.extremetech.com/g00/extreme/215170-artificial-neural-networks-are-changing-the-world-what-are-they?i10c.referrer=https%3A%2F%2Fwww.google.com%2F Preview - Slide Available at deeplearningconf.com
- 3. Sam Putnam @edeeplearning Deploying EnterpriseDeep Learning And This Song! Preview - Slide Available at deeplearningconf.com
- 4. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning And Predicted These Housing Prices! https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb http://www.mirror.co.uk/news/uk-news/experts-predict-house-prices-could-10724816 Run the Code -> This Afternoon
- 5. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning OK, so what is Deep Learning? http://p.migdal.pl/imgs/2017-04-30-learning-deep-learning/deep_learning_meme_keras.png Preview - Slide Available at deeplearningconf.com
- 6. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Deep Learning is Neural Networks http://p.migdal.pl/imgs/2017-04-30-learning-deep-learning/deep_learning_meme_keras.png Preview - Slide Available at deeplearningconf.com
- 7. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Well, Artificial Neural Networks. But they are based off of how the brain works. https://www.extremetech.com/g00/extreme/215170-artificial-neural-networks-are-changing-the-world-what-are-they?i10c.referrer=https%3A%2F%2Fwww.google.com%2F Preview - Slide Available at deeplearningconf.com
- 8. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning This is a Neuron: Electrical Signals Are Sent By Axons and Received By Dendrites http://www.newworldencyclopedia.org/entry/Dendrite Preview - Slide Available at deeplearningconf.com
- 9. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning This is a Single Artificial Neuron With Two Inputs and Two Weights Preview - Slide Available at deeplearningconf.com
- 10. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning The Artificial Neuron is Also Called a “Perceptron”. It “Perceives” Inputs. Preview - Slide Available at deeplearningconf.com
- 11. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning b is the bias, and to activate the neuron:
- 12. Sam Putnam @edeeplearning Deploying EnterpriseDeep Learning Click Play, Visualize Neuron’s Performance http://playground.tensorflow.org/
- 13. Sam Putnam @edeeplearning Deploying EnterpriseDeep Learning Trust Me, When you add more inputs, your decision boundary becomes a “plane” Preview - Slide Available at deeplearningconf.com
- 14. When you’re talkingabout Deep Learning you’re talking about Artificial Neural Networks of Neurons @edeeplearning Preview - Slide Available at deeplearningconf.com
- 15. In particular, DeepLearning = Deep Artificial Neural Networks of Neurons @edeeplearning Preview - Slide Available at deeplearningconf.com
- 16. This it it- Just more than one layer of neurons between the input and output! @edeeplearninghttp://blog.christianperone.com/2015/08/convolutional-neural-networks-and-feature-extraction-with-python/ Preview - Slide Available at deeplearningconf.com
- 17. You Tried TheLine, Now Add A Hidden Layer To Capture More Complex Data Separations @edeeplearninghttp://blog.christianperone.com/2015/08/convolutional-neural-networks-and-feature-extraction-with-python/
- 18. This is NoLine You Are Tweaking! This Is a More Complex Function @edeeplearninghttp://blog.christianperone.com/2015/08/convolutional-neural-networks-and-feature-extraction-with-python/ Preview - Slide Available at deeplearningconf.com
- 19. More Neurons +A Deeper Network = More Sophisticated Representations @edeeplearninghttps://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground
- 20. Look at thelast hidden layer - it is doing a pretty good job separating the data @edeeplearninghttps://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground Preview - Slide Available at deeplearningconf.com
- 21. This Architecture iscalled a Feedforward Neural Network @edeeplearninghttps://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground Preview - Slide Available at deeplearningconf.com
- 22. There’s a LotGoing on Here. Do You Really Want to Change Those Weights One at a Time? @edeeplearninghttps://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground Preview - Slide Available at deeplearningconf.com
- 23. Solution: Gradient Descent- Look How the Lowest Loss (0) Is Sought Out on the Right @edeeplearninghttps://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground
- 24. What other ArchitecturesAre There? Well, what about for Time Series Data @edeeplearninghttps://deeplearning4j.org/lstm.html Preview - Slide Available at deeplearningconf.com
- 25. Recurrent Neural Networksare Networks with Loops in Them @edeeplearninghttp://colah.github.io/posts/2015-08-Understanding-LSTMs/ Preview - Slide Available at deeplearningconf.com
- 26. Information is passedfrom one step of the network to the next @edeeplearninghttp://colah.github.io/posts/2015-08-Understanding-LSTMs/ Preview - Slide Available at deeplearningconf.com
- 27. Recurrent Neural Networks(RNNs) Contain A Single Layer (Yellow Box) @edeeplearninghttp://colah.github.io/posts/2015-08-Understanding-LSTMs/
- 28. Recurrent Neural Networks(RNNs) cannot handle long term dependencies @edeeplearninghttp://colah.github.io/posts/2015-08-Understanding-LSTMs/ Preview - Slide Available at deeplearningconf.com
- 29. LSTM Recurrent NeuralNetworks (RNNs) Contain Four Layers (Yellow Box) @edeeplearninghttp://colah.github.io/posts/2015-08-Understanding-LSTMs/
- 30. Long Short TermMemory Networks (LSTMs) Can Handle Long Term Dependencies @edeeplearninghttp://colah.github.io/posts/2015-08-Understanding-LSTMs/ Preview - Slide Available at deeplearningconf.com
- 31. @edeeplearning Sigmoid Activation FunctionScales Your Input Between 0 and 1 http://neuralnetworksanddeeplearning.com/chap4.html Preview - Slide Available at deeplearningconf.com
- 32. Sigmoid Activation FunctionScales Your Input Between 0 and 1 @edeeplearning http://neuralnetworksanddeeplearning.com/chap4.html Preview - Slide Available at deeplearningconf.com
- 33. Tanh Activation FunctionScales Your Input Between -1 and 1 @edeeplearning http://neuralnetworksanddeeplearning.com/chap4.html Preview - Slide Available at deeplearningconf.com
- 34. RELU Activation FunctionCuts off Negative Inputs @edeeplearning http://neuralnetworksanddeeplearning.com/chap4.html Preview - Slide Available at deeplearningconf.com
- 35. What About ImageData? For That, Convolutional Neural Networks (Right) @edeeplearninghttp://cs231n.github.io/convolutional-networks/
- 36. Convolution Just MeansPicking Out Representative Elements from a Bunch of Pixels (F represents all the below) @edeeplearning Preview - Slide Available at deeplearningconf.com
- 37. This is WhatConvolution Looks Like, a Little Boring, But Look How It Reduces the Size of the Data (3x3 on the Right!) @edeeplearning Preview - Slide Available at deeplearningconf.com
- 38. So Convolution Isa Mathematical Operation (Using a Weight Filter, Let’s Look!), but Even Simpler is Max Pooling, just take the Biggest Pixel! @edeeplearning
- 39. A Pre-Trained ImageRecognition Neural Network Looks Like This (Input At Left) - Let’s Zoom In @edeeplearninghttps://arxiv.org/pdf/1409.4842.pdf Preview - Slide Available at deeplearningconf.com
- 40. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Deep Learning in Production Considerations Help me find the source for this nice diagram? Lost it :(
- 41. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Solution - Use What I Know and Am Allowed To Use, i.e. Kaggle, AWS, TF, Jupyter Help me find the source for this nice diagram? Lost it :( Preview - Slide Available at deeplearningconf.com
- 42. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning What I Did - A Real Estate Agent Told Me About His Problem & Pointed Me To The Data https://www.fortunebuilders.com/how-to-become-a-real-estate-agent/ Preview - Slide Available at deeplearningconf.com
- 43. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning I Took a Holdout Set of the Data and Left the Rest for Train/Validate https://www.fortunebuilders.com/how-to-become-a-real-estate-agent/
- 44. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Started with Feature Selection and then Linear Regression in Good old Excel, WITH the subject matter expert (the real estate agent). Did Pretty Well. https://www.youtube.com/watch?v=SQkpLMLoqww Preview - Slide Available at deeplearningconf.com
- 45. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Preview - Slide Available at deeplearningconf.com How Well? 18 percent Mean Error and 26 percent Median Error before Feature Selection using Weighted Importance, Did some thinking:
- 46. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Feature Importance, for Feature Selection, Often Use After Deep Learning Step, to Gain Interpretability
- 47. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning https://www.kaggle.com/samdeeplearning/naive-subsample-5-10-city/output GBM (Boosted Decision Tree) - with Hyperparameter Tweaking - Yields Strong Model for this Problem
- 48. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning https://www.kaggle.com/samdeeplearning/naive-subsample-0-25-xgb/notebook Preview - Slide Available at deeplearningconf.com Starts to Degrade Upon Subsampling only 1/4 of the Trees
- 49. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning
- 50. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Want To See If Neural Network Can Improve Performance
- 51. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Building off of a preexisting housing prices regression model https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb Preview - Slide Available at deeplearningconf.com
- 52. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Set Input Layer (n_hidden_1 is input layer here) to number of features (27) https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb Preview - Slide Available at deeplearningconf.com
- 53. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Combining the features right off the bat by using a wide first hidden layer (200 nodes) https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb Preview - Slide Available at deeplearningconf.com
- 54. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Set number of classes to 1 for regression problem https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb Preview - Slide Available at deeplearningconf.com
- 55. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Multilayer Perceptron = Feedforward Perceptron We Looked At Before https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb Preview - Slide Available at deeplearningconf.com
- 56. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning RELU - As Mentioned in Executing Strategies Yesterday, not using negative features/weights https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb
- 57. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Training - Can tell performance needs tweaking, pretty good on houses 2-6 https://github.com/EnterpriseDeepLearning/housing-prices-wide-and-deep/blob/master/real_estate.ipynb 1 2 34 5 6 Estimate on LeftPrediction on Left
- 58. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Let’s Dive Into the TensorFlow Housing Code https://www.kaggle.com/samdeeplearning/deep-neural-network-for-starters-r/edit Preview - Slide Available at deeplearningconf.com
- 59. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning What Next? Go Deep Learning! https://www.youtube.com/watch?v=cSKfRcEDGUs&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal&index=6
- 60. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning 1) Identify an architecture for your problem https://www.youtube.com/watch?v=cSKfRcEDGUs&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal&index=6
- 61. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning 2) Identify a Framework, Be Willing to Try a New Framework https://blog.algorithmia.com/deploying-deep-learning-cloud-services/
- 62. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning 3) Build or tailor a model for your application, Don’t be Afraid to Reference Academic Code https://www.youtube.com/watch?v=cSKfRcEDGUs&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal&index=6
- 63. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning 4)Tweak the Parameters for your Model https://www.youtube.com/watch?v=cSKfRcEDGUs&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal&index=6
- 64. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning 5)Train on a (probably, Nvidia, maybe Intel) GPU if need be (for image data, you need it) https://www.youtube.com/watch?v=2NrgPdGSXhE
- 65. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning 6) Validate, test, check in the real world. Iterate! https://medium.com/towards-data-science/train-test-split-and-cross-validation-in-python-80b61beca4b6 Preview - Slide Available at deeplearningconf.com
- 66. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Weekend trial: Josh Gordon’s Machine Learning Recipes. If the code doesn’t work for you (I am 99% sure it will), let me know! https://www.youtube.com/watch?v=cSKfRcEDGUs&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal&index=6 Preview - Slide Available at deeplearningconf.com
- 67. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Like R? Try this notebook on my Kaggle https://www.kaggle.com/samdeeplearning/deep-neural-network-for-starters-r/edit
- 68. Sam Putnam @edeeplearning July 27 DeployingEnterprise Deep Learning Submit your predictions, even! (disclaimer: middle of the pack result) https://www.kaggle.com/samdeeplearning/deep-neural-network-for-starters-r/edit Preview - Slide Available at deeplearningconf.com
- 69. Sam Putnam Thank youto Google and others who have published diagrams and photos. Slides are for today only. @edeeplearning Questions/Comments: Sam@EDeepLearning.com Thank you July 27 Deploying Enterprise Deep Learning