Machine Learning for Developers

Codemotion, Rome, March 25th, 2017

Have you always wanted to add predictive capabilities or voice recognition to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. This session will help you understand machine learning terminology & challenges, implement a machine learning model, add predictive capabilities to your app, and provide your customer with voice UX.

Transcript

1. Machine Learning
   For Developers
   Danilo Poccia
   @danilop danilop
   AWS Technical Evangelist
   

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2. Credit: Gerry Cranham/Fox Photos/Getty Images
   http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/
   

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3. 1939 London Underground
   Credit: Gerry Cranham/Fox Photos/Getty Images
   http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/
   

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4. Data Predictions
   

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5. Data Model Predictions
   

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6. Model
   

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7. http://www.thehudsonvalley.com/articles/60-years-ago-today-local-technology-demonstrated-artificial-intelligence-for-the-first-time
   1959 Arthur Samuel
   

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8. Machine Learning
   

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9. Machine Learning
   Supervised
   Learning
   Inferring a model
   from labeled
   training data
   

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10. Machine Learning
    Supervised
    Learning
    Unsupervised
    Learning
    Inferring a model
    from labeled
    training data
    Inferring a model
    to describe hidden
    structure from
    unlabeled data
    

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11. Reinforcement
    Learning
    Perform a certain
    goal in a
    dynamic
    environment
    Machine Learning
    Supervised
    Learning
    Unsupervised
    Learning
    

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12. Driving a vehicle
    Playing a game
    against an opponent
    

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13. Clustering
    

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14. Clustering
    

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15. Tip: Try topic modeling with your own emails ;-)
    Topic Modeling
    Discovering abstract “topics”
    that occur in a collection of documents
    For example, looking for “infrequent” words
    that are used more often in a document
    

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16. Regression “How many bikes will
    be rented tomorrow?”
    Happy, Sad, Angry,
    Confused, Disgusted,
    Surprised, Calm,
    Unknown
    Binary
    Classification
    Multi-Class
    Classification
    “Is this email spam?”
    “What is the
    sentiment of this
    tweet, or of this social
    media comment?”
    1, 0, 100K
    Yes / No
    True / False
    %
    

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17. Training the Model
    Minimizing the Error
    of using the Model on the Labeled Data
    

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18. Validation
    How well is this Model working on New Data?
    

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19. Be Careful of Overfitting
    

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20. Be Careful of Overfitting
    

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21. Be Careful of Overfitting
    

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22. Better Fitting
    

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23. Better Fitting
    

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24. Different Models ⇒ Different Predictions
    

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25. Labeled Data
    

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26. Labeled Data
    70%
    30%
    Training
    Validation
    

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27. Neural
    Networks
    

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28. 1943 Warren McCulloch, Walter Pitts
    Threshold
    Logic
    Units
    

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29. 1962 Frank Rosenblatt
    Perceptron
    

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30. ∑
    w1
    w2
    w3
    wn
    w0
    =
    output
    weights
    (parameters)
    activation
    function
    input
    

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31. f(∑)
    w1
    w2
    w3
    wn
    w0
    =
    weights
    (parameters)
    activation
    function
    output
    input
    

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32. f(∑)
    input output
    

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33. 1969 Marvin Minsky, Seymour Papert
    Perceptrons:
    An Introduction
    to Computational Geometry
    A perceptron can only solve
    linearly separable functions
    (e.g. no XOR)
    

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34. f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    input
    layer
    hidden
    layer
    output
    layer
    input output
    Multiple Layers
    Lots of Parameters
    Backpropagation
    

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35. Microprocessor Transistor Counts 1971-2011
    Intel E7 CPU
    4-24 cores
    NVIDIA K80 GPU
    2,496 cores
    https://en.wikipedia.org/wiki/Moore's_law
    

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36. LeCun, Gradient-Based
    Learning Applied to Document
    Recognition,1998
    Hinton, A Fast Learning
    Algorithm for Deep Belief
    Nets, 2006
    Bengio, Learning Deep
    Architectures for AI, 2009
    Advances in Research 1998-2009
    

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37. Image
    Processing
    

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38. f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    f(∑)
    output
    How to give images in input
    to a Neural Network?
    Photo by David Iliff. License: CC-BY-SA 3.0
    https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
    

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39. Convolution Matrix
    0 0 0
    0 1 0
    0 0 0
    Identity
    Photo by David Iliff. License: CC-BY-SA 3.0
    https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
    

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40. Convolution Matrix
    1 0 -1
    2 0 -2
    1 0 -1
    Left Edges
    Photo by David Iliff. License: CC-BY-SA 3.0
    https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
    

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41. Convolution Matrix
    -1 0 1
    -2 0 2
    -1 0 1
    Right Edges
    Photo by David Iliff. License: CC-BY-SA 3.0
    https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
    

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42. Convolution Matrix
    1 2 1
    0 0 0
    -1 -2 -1
    Top Edges
    Photo by David Iliff. License: CC-BY-SA 3.0
    https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
    

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43. Convolution Matrix
    -1 -2 -1
    0 0 0
    1 2 1
    Bottom Edges
    Photo by David Iliff. License: CC-BY-SA 3.0
    https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
    

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44. Convolution Matrix
    0.6 -0.6 1.2
    -1.4 1.2 -1.6
    0.8 -1.4 1.6
    Random Values
    Photo by David Iliff. License: CC-BY-SA 3.0
    https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
    

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45. Convolutional Neural Networks (CNNs)
    https://en.wikipedia.org/wiki/Convolutional_neural_network
    

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46. ImageNet Classification Error Over Time
    0
    5
    10
    15
    20
    25
    30
    2010 2011 2012 2013 2014 2015 2016
    Classification Error
    CNNs
    

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47. 2012 ImageNet Classification with Deep Convolutional Neural Networks
    

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48. SuperVision: 8 layers, 60M parameters
    0
    

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49. 2013 Visualizing and Understanding Convolutional Networks
    

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52. http://www.asimovinstitute.org/neural-network-zoo/
    Lots of Parameters
    Network Architectures
    defined by Hyperparameters
    Dropout Layers
    for Regularization
    

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53. Generative Adversarial Networks (GANs)
    Generator
    Neural
    Network
    Discriminator
    Neural
    Network
    Real or
    Generated?
    Real
    Picture
    Generated
    Picture
    

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54. 2016
    Generative Adversarial Networks (GANs)
    

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55. Artificial Intelligence & Deep Learning At Amazon
    Thousands Of Employees Across The Company Focused on AI
    Discovery &
    Search
    Fulfilment &
    Logistics
    Add ML-powered
    features to existing products
    Echo &
    Alexa
    

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56. Artificial Intelligence on AWS
    P2, F1 &
    Elastic GPUs
    Deep Learning
    AMI and template
    Investment in
    Apache MXNet
    

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57. Apache MXNet
    

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58. Deep Learning Frameworks
    MXNet, Caffe, Tensorflow, Theano,
    Torch, CNTK and Keras
    Pre-installed components to speed
    productivity, such as Nvidia drivers, CUDA,
    cuDNN, Intel MKL-DNN with MXNet,
    Anaconda, Python 2 and 3
    AWS Integration
    Deep Learning AMI
    

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59. Amazon AI
    Bringing Powerful Artificial Intelligence To All Developers
    

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60. Amazon Rekognition
    Image Recognition And Analysis
    Powered By Deep Learning
    1
    

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61. Amazon Rekognition
    Deep learning-based image recognition service
    Search, verify, and organize millions of images
    Object and Scene
    Detection
    Facial
    Analysis
    Face
    Comparison
    Facial
    Recognition
    

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62. Amazon Rekognition: Images In,
    Categories and Facial Analysis Out
    Amazon
    Rekognition
    Car
    Outside
    Daytime
    Driving
    Objects
    & Scenes
    Female
    Smiling
    Sunglasses
    Face ID
    DetectLabels
    DetectFaces
    CompareFaces
    IndexFaces
    SearchFacesByImage
    Faces
    

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64. Deep Learning Process
    Conv 1 Conv 2 Conv n
    …
    …
    Feature Maps
    Labrador
    Dog
    Beach
    Outdoors
    Softmax
    Probability
    Fully
    Connected
    Layer
    

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65. Bynder allows you to easily create, find and use content
    for branding automation and marketing solutions.
    With our new AI capabilities,
    Bynder’s software… now allows
    users to save hours of admin
    labor when uploading and
    organizing their files, adding
    exponentially more value.
    Chris Hall
    CEO, Bynder
    ”
    “
    With Rekognition, Bynder revolutionizes marketing admin tasks with AI capabilities
    

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66. Amazon Polly
    Text To Speech Powered By Deep Learning
    2
    

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67. Amazon Polly: Text In, Life-like Speech Out
    Amazon Polly
    “The temperature
    in WA is 75°F”
    “The temperature
    in Washington is 75 degrees
    Fahrenheit”
    

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68. TEXT
    Market grew by > 20%.
    WORDS
    PHONEMES
    {
    {
    {
    {
    {
    ˈtwɛn.ti
    pɚ.ˈsɛnt
    ˈmɑɹ.kət ˈgɹu baɪ ˈmoʊɹ
    ˈðæn
    PROSODY CONTOUR
    UNIT SELECTION AND ADAPTATION
    TEXT PROCESSING
    PROSODY MODIFICATION
    STREAMING
    Market grew by more
    than
    twenty
    percent
    Speech units
    inventory
    

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69. aws polly synthesize-speech
    --text "It was nice to live such a wonderful live show."
    --output-format mp3
    --voice-id Joanna
    --text-type text
    output.mp3
    

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70. “Nel mezzo del cammin di nostra vita
    mi ritrovai per una selva oscura
    ché la diritta via era smarrita.”
    https://commons.wikimedia.org/wiki/File:Portrait_de_Dante.jpg
    

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71. Duolingo voices its language learning service Using Polly
    Duolingo is a free language learning service where users
    help translate the web and rate translations.
    With Amazon Polly our users
    benefit from the most lifelike
    Text-to-Speech voices
    available on the market.
    Severin Hacker
    CTO, Duolingo
    ”
    “ • Spoken language crucial for
    language learning
    • Accurate pronunciation matters
    • Faster iteration thanks to TTS
    • As good as natural human speech
    

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72. GoAnimate is a cloud-based, animated video creation
    plarform.
    Amazon Polly gives
    GoAnimate users the ability to
    immediately give voice to the
    characters they animate using
    our platform.
    Alvin Hung
    CEO, GoAnimate
    ”
    “ • Multi-language communication
    • Training or HR professionals who
    have to create content in many
    languages
    • Video preproduction
    • Video makers who need to iterate
    and fine-tune before the text-to-
    speech is eventually replaced by a
    professional voiceover
    • K–12 education
    • Students who make videos and don’t
    have access to professional voices
    or time for or knowledge of voiceover
    With Polly, GoAnimate gives voice to the characters in their animations
    

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73. ”
    “
    Royal National Institute of Blind People creates and
    distributes accessible information in the form of
    synthesized content
    Amazon Polly delivers
    incredibly lifelike voices which
    captivate and engage our
    readers.
    John Worsfold
    Solutions Implementation Manager, RNIB
    • RNIB delivers largest library of
    audiobooks in the UK for nearly 2 million
    people with sight loss
    • Naturalness of generated speech is
    critical to captivate and engage readers
    • No restrictions on speech redistributions
    enables RNIB to create and distribute
    accessible information in a form of
    synthesized content
    RNIB provides the largest library in the UK for people with sight loss
    

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74. Amazon ALEXA
    (It’s what’s inside Alexa)
    3
    Natural Language Understanding (NLU) &
    Automatic Speech Recognition (ASR) Powered By Deep Learning
    

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75. Amazon Lex: Speech Recognition
    & Natural Language Understanding
    Amazon Lex
    Automatic Speech Recognition
    Natural Language Understanding
    “What’s the weather
    forecast?”
    Weather
    Forecast
    

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76. Amazon Lex: Speech Recognition
    & Natural Language Understanding
    Amazon Lex
    Automatic Speech Recognition
    Natural Language Understanding
    “What’s the weather
    forecast?”
    “It will be sunny
    and 25°C”
    Weather
    Forecast
    

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77. Lex Bot Structure
    Utterances
    Spoken or typed phrases that invoke your
    intent
    BookHotel
    Intents
    An Intent performs an action in response
    to natural language user input
    Slots
    Slots are input data required to fulfill the
    intent
    Fulfillment
    Fulfillment mechanism for your intent
    

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78. Hotel Booking
    City New York City
    Check In Nov 30th
    Check Out Dec 2nd
    Hotel Booking
    City New York City
    Check In
    Check Out
    “Book a Hotel”
    Book Hotel
    NYC
    “Book a Hotel in
    NYC”
    Automatic Speech
    Recognition
    Hotel Booking
    New York City
    Natural Language
    Understanding
    Intent/Slot
    Model
    Utterances
    “Your hotel is booked
    for Nov 30th”
    Polly
    Confirmation: “Your hotel is
    booked for Nov 30th”
    a
    in
    “Can I go ahead with
    the booking?”
    

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79. ”
    “
    Finding missing persons:
    ~100,000 active missing
    persons cases in the U.S.
    at any given time
    ~60% are adults,
    ~40% are children
    • Motorola Solutions applies Amazon
    Rekognition, Amazon Polly and Amazon
    Lex
    • Image analytics and facial recognition
    can continually monitor for missing
    persons
    • Tools that understand natural language
    can enable officers to keep eyes up and
    hands free
    Motorola Solutions is using Amazon AI to help finding missing persons
    Motorola Solutions keeps utility workers connected and
    visible to each other with real-time voice and data
    communication across the smart grid.
    

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80. 
    I See
    
    

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81. I see…
    Amazon
    Rekognition
    Amazon
    Polly
    Camera
    Raspberry Pi
    Voice
    Synthesize
    Speech
    Detect Labels
    Detect Faces
    

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83. Nikola Tesla, 1926
    “When wireless is perfectly
    applied, the whole earth will be
    converted into a huge brain…”
    

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84. Machine Learning
    For Developers
    Danilo Poccia
    @danilop danilop
    AWS Technical Evangelist
    

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