Intro to Deep Learning for Core ML

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1 Intro to Deep Learning for Core ML It s Difficult to Make Predictions. Especially About the Consultant 1

2 Core ML "With Core ML, you can integrate trained machine learning models into your app." -- Apple 2

3 What is a model? An artifact created by training a machine learning algorithm. Basically a file with a bunch of numbers and some meta 3

4 What even is Machine 4

5 Artificial Intelligence Artificial Intelligence (AI) - the study of "intelligent agents". Reasoning, knowledge representation, planning, robotics, etc. Artificial Narrow Intelligence (ANI) Artificial General Intelligence (AGI) Artificial Superintelligence 5

6 Machine Learning Machine Learning (ML) - Programs that learn from the data and make predictions. Tree Ensembles Support Vector Machines Generalized Linear Models Deep Neural 6

7 Deep Learning Deep Learning (DL) - ML/AI using artificial neural networks 7

8 Hype or 8

9 "It is a renaissance, it is a golden age," "Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy basically there s no institution in the world that cannot be improved with machine learning." 9

10 Microso! Last year "Our strategy is to build best-in-class platforms and productivity services for a mobile-first, cloud-first world." Form 10K Now "Our strategy is to build best-in-class platforms and productivity services for an intelligent cloud and an intelligent edge infused with artificial intelligence ( AI )." Form 10

11 Investments in AI Microsoft - MS Research AI Lab, CNTK Intel - Neon, Nervana Google - DeepMind, Google Brain, PAIR, TF Facebook - FAIR, PyTorch, Caffe2 Amazon - GPU instances, MXNet Apple - Core ML, Siri, car, maps, AR, blog China - AI leadership by 2030 Canada,... and everyone 11

12 Every industry can expect to be transformed by Artificial 12

13 Healthcare "Near or better than human level 13

14 Performance ML models make mistakes Humans (experts) make mistakes Experts don't agree with other experts Experts don't agree with themselves ML can augment human 14

15 Applications Text, audio, image, video understanding User intent predictions Recommendations Games - asset generation, character control Manufacturing, maintenance and control Many many 15

16 Not 16

17 Intended 17

18 Is this a hot 18

19 Is this a hot 19

20 Is this a hot 20

21 Is this a hot 21

22 Is this a hot 22

23 Image Classification Justin Johnson, Andrej Karpathy, Li Fei-Fei

24 Imagenet 14,197,122 images in 21,841 (?) 24

25 Object 25

26 Image 26

27 Dense 27

28 How do they do it? Where do machine learning models come from? Libraries for decision trees, ensembles, etc. scikit-learn XGBoost 28

29 The New Shiny: Deep Learning Core ML calls out Caffe Keras Also: Tensorflow, Theano, MXNet, CNTK, PyTorch, Neon, 29

30 Third time is a charm Dramatic improvements due to advancements in: Data Algorithms 30

31 Steps for your ML project Definition Prep Training Prediction (inference, scoring) / 31

32 Problem Definition Types of business questions How much? - Regression What is it? - Classification What now? - Reinforcement learning What is our measure of success? - Error 32

33 Data Prep What data do I have or can get? Why do I think it is useful? What biases are in it? How does it need to be 33

34 Don't underestimate the 34

35 Our Demo Data: Wine Quality 35

36 Types of Features Numeric in similar ranges numbers - scaled to ~ (-1,1) categorical - "1 hot" encoded, vector embedding text - word2vec, Glove, custom embedding dates - Unix time, DOW, MOY, 36

37 Types of data Labeled - supervised Unlabeled

38 Building a NN to 38

39 Neurons: Biologically inspired 1942 McCulloch and Pitts 1957 Rosenblatt for i in len(w): o = x[i] * w[i] o = o + b return A(o) A(zip(x, w).map(*).reduce(0, +)

40 Activation Function Introduces non linearity Historically: Step, Sigmoid, Tanh Commonly: Rectified linear Unit (relu), 40

41 Universal Approximation Theorem (1989)... a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous 41

42 Deep Neural Nets A net with more than one hidden 42

43 VGG ,357,

44 GoogleLeNet 44

45 Training 0) Pick an architecture 1) Initialize weights randomly 2) Make prediction 3) Measure error (loss) 4) Adjust weights in the right direction 5) GOTO 45

46 Gradient Descent To know the right direction calculate the gradient of the loss function with respect to each 46

47 Backpropagation Use the chain rule. f(x) = g(h(x)) f'(x) = g'(h(x)) h'(x) 1) Feed the signal forward through the network 2) Propagate the error back across the network. Don't worry. The libraries do it for 47

48 Millions of Knobs 48

49 49

50 Our Demo Data: Wine Quality Data 11 features, 1 target column,

51 UCI Machine Learning Repository Source: Paulo Cortez, University of Minho, Guimarães, Portugal, h!p://www3.dsi.uminho.pt/ pcortez A. Cerdeira, F. Almeida, T. Matos and J. Reis, Viticulture Commission of the Vinho Verde Region(CVRVV), 51

52 A Simple Neural Net in Keras model = Sequential() # input layer model.add(dense(16,input_dim=11,activation='relu')) # hidden layer model.add(dense(8,activation='relu')) # output layer 52

53 53

54 Cool Demo Bro But thats a long way from a cat riding a 54

55 How Do We Work With Images Well, images are just numbers/data. Though numbers close to each other are more 55

56 Convolutional Layers Similar to correlations from signal processing or filters from photoshop. A small NxN filter is slid over and convolved/correlated with the image. Learns to find features. Then lower level features are combined into higher level 56

57 Types of ANN (layers) 1. Dense Neural Net (DNN) - fully connected 2. Convolutional Neural Net (CNN) - image/2d data 3. Recurrent Neural Net (RNN) - time series, sequential 4. Everything else - mostly innovative architectures and 57

58 Want to add AI/ML to your projects? Options API calls to third party service Use traditional ML models Fine tune existing model (transfer learning) Create your own custom DL model Some combination of all of 58

59 Challenges with DL Needs lots of data. Labeled data is expensive. Lacks explainability Computational requirements - training and inference Performance limits unclear Best architecture 59

60 Benefits of DL Handles much of the feature engineering Handles complex (non linear) problems Advancements coming 60

61 Think carefully about Your business question How you'll measure success Gathering relevant data Compensating for biases Handling errors Managing changes in production Updating models (online 61

62 Recommendations Do not be intimidated the math. Start with Keras (w/tensorflow) or maybe Pytorch. Later choose language/framework as needs 62

63 Resources Andrew Ng's Coursera and Fast.AI courses Deep Learning Book - Goodfellow, Bengio and Courville Meetups - Portland-Data-Science-Group - Portland-Machine-Learning-Meetup - Portland-Deep-Learning 2 2 I run this 63

64 Thank 64

65 Programming Abstractions Level Python ios Prediction Keras Core ML Training Computation Graph, Backprop, Autograd Keras Tensorflow, Caffe Matrix Math CUDA, Eigen3 Metal, 65

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