Machine Learning Melanie Warrick @nyghtowl

Who am I?

@nyghtowl Input Computation Result Artificial Intelligence

@nyghtowl AI Fields ● Machine Learning ● Statistics & Probability Models ● Generative & Adversarial Models ● Graph Theory ● Quantum Computing

@nyghtowl Artificial Intelligence Machine Learning Deep Learning

@nyghtowl Machine Learning * Rosenfeld Media

@nyghtowl “DUKE VINCENTIO: Well, your wit is in the care of side and that. Second Lord: They would be ruled after this chamber, and my fair nues begun out of the fact, to be conveyed, Whose noble souls I'll have the heart of the wars. Clown: Come, sir, I will make did behold your worship. VIOLA: I'll drink it.” - Karpathy: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ - Shakespeare image {{PD-US}}

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@nyghtowl Machine Learning...

@nyghtowl ML Algorithms ● Linear & Logistic Regression ● SVM ● Random Forest ● Neural Networks ● Reinforcement Learning

@nyghtowl Linear Model / Equation y = mx + b Linear Regression Model Example coefficients

@nyghtowl Activation Func: ● sigmoid ● rectified linear ● softmax ● binary step Artificial Neural Net y = (W*x + b)

@nyghtowl Deep Neural Nets Hidden Input Layer 2 Layer 3 Layer 4

@nyghtowl Convolutional Neural Networks Source: mNeuron: A Matlab Plugin to Visualize Neurons from Deep Models, Donglai Wei et. al.

@nyghtowl Supervised Learning Test Train

@nyghtowl Minimize Loss Function Loss Functions ● mean sqr. error ● negative log likelihood ● cross entropy Error classific. vs. real label dog vs. flower Output

@nyghtowl Optimization | Backprop Run until error stops improving = converge flower

@nyghtowl Evaluation NN

@nyghtowl Google Cloud Platform Virtual Machines Data Storage Analysis & Pipelines Machine Learning APIs & Engine w/

@nyghtowl ML Pipeline | Training DB Data Train Algorithm(s) Test Model DB Serial Model Raw Data Cloud SQL Compute Engine protobuf Container Engine Cloud Storage Cloud Dataproc

@nyghtowl ML Pipeline | Production Data Serial New Data Model Cloud Pub/Sub Cloud Dataflow Kafka Compute Engine Container Engine Mobile apps

@nyghtowl Machine Learning Pipeline DB Data Train Algorithm(s) Test Model DB Serial Data Model Serial Raw Data New Data Model Cloud Pub/Sub Cloud Dataflow Kafka Cloud SQL Compute Engine Training Production protobuf Container Engine Cloud Storage Cloud Dataproc Mobile apps

@nyghtowl Cifar -10 ● 60K images ● 10 classes ● 32 x 32 pixels (each one is an input) - CIFAR-10 http://www.cs.toronto.edu/~kriz/cifar.html | Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009 - Kaggle https://www.kaggle.com/c/cifar-10

Video Intelligence https://github.com/sararob/video-intelligence-demo

@nyghtowl Last Points ML in real world applications Leaders in field drive stable solutions Never trust the data or models

@nyghtowl Resources & References ● How to run any ML algorithm on GCP nyghtowl.com ● Gorner, Martin (2017 Jan 19) Learn TensorFlow and deep learning without a Ph.D. https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd ● Muic, Tommy (2016 Sep 25) Exception Models explained and implemented https://hacktilldawn.com/2016/09/25/inception-modules-explained-and-implemented/ ● Lewis-Kraus, Gideon (2016 Dec. 14) The Great AI Awakening https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?smprod=nytcore-ipad&smid=nytcore-ipad-sha re&_r=0 ● Nielsen, Michael Neural Nets and Deep Learning http://neuralnetworksanddeeplearning.com/ ● Karpathy, Andrej (2015) Hacker’s Guide to Neural Nets https://karpathy.github.io/neuralnets/ ● Convolutional Networks for Visual Recognition http://cs231n.github.io/ ● Hobbs, Paul (2013 Oct 4) How can the various machine learning algorithms be classified summarized... https://www.quora.com/How-can-the-various-machine-learning-algorithms-be-classified-summarized-according-to-the-probl ems-they-solve ● Chollet, Francois (2017 July 18) The limitations of deep learning https://blog.keras.io/the-limitations-of-deep-learning.html “All code snippets in this presentation are licensed under Apache 2.0 license” ● Keras:https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py ● Keras model to TF:https://github.com/amir-abdi/keras_to_tensorflow/blob/master/keras_to_tensorflow.ipynb

@nyghtowl References: Images ● iStock.com/adventtr ● iStock.com/4x-image ● iStock.com/vectortatu ● iStock.com/d1sk ● iStock.com/georgeclerk ● iStock.com/JurgaR ● iStock.com/ranckreporter ● https://static.pexels.com/photos/6069/grass-lawn-green-wooden-6069.jpg ● https://www.kaggle.com/c/cifar-10 & http://www.cs.toronto.edu/~kriz/cifar.htm ● https://en.wikipedia.org/wiki/Precision_and_recall ● https://www.digitaltrends.com/mobile/google-lens-google-io-2017/ ● Google IO ● https://code.flickr.net/2014/10/20/introducing-flickr-park-or-bird/ ● http://www.texample.net/tikz/examples/neural-network/ ● http://www.texample.net/tikz/examples/neural-network/ ● https://en.wikipedia.org/wiki/General_linear_model ● https://en.wikipedia.org/wiki/Convolutional_neural_network ● https://commons.wikimedia.org/wiki/File:Perceptron.png ● https://en.wikipedia.org/wiki/File:Shakes.png ● https://www.computer.org/web/awards/pioneer-jean-sammet ● Rosenfeld Media https://www.flickr.com/photos/rosenfeldmedia/6949089460 ● Copyright and disclaimer notice: https://creativecommons.org/licenses/by/2.0/ ● License notice: https://creativecommons.org/licenses/by/2.0/legalcode

@nyghtowl Machine Learning Melanie Warrick @nyghtowl