Machine Learning for Developers

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.

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Danilo Poccia

March 25, 2017
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  1. Machine Learning For Developers Danilo Poccia @danilop danilop AWS Technical

    Evangelist
  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/

  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/

  4. Data Predictions

  5. Data Model Predictions

  6. Model

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

  8. Machine Learning

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

    data
  10. Machine Learning Supervised Learning Unsupervised Learning Inferring a model from

    labeled training data Inferring a model to describe hidden structure from unlabeled data
  11. Reinforcement Learning Perform a certain goal in a dynamic environment

    Machine Learning Supervised Learning Unsupervised Learning
  12. Driving a vehicle Playing a game against an opponent

  13. Clustering

  14. Clustering

  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
  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 %
  17. Training the Model Minimizing the Error of using the Model

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

  19. Be Careful of Overfitting

  20. Be Careful of Overfitting

  21. Be Careful of Overfitting

  22. Better Fitting

  23. Better Fitting

  24. Different Models ⇒ Different Predictions

  25. Labeled Data

  26. Labeled Data 70% 30% Training Validation

  27. Neural Networks

  28. 1943 Warren McCulloch, Walter Pitts Threshold Logic Units

  29. 1962 Frank Rosenblatt Perceptron

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

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

    function output input
  32. f(∑) input output

  33. 1969 Marvin Minsky, Seymour Papert Perceptrons: An Introduction to Computational

    Geometry A perceptron can only solve linearly separable functions (e.g. no XOR)
  34. f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) input

    layer hidden layer output layer input output Multiple Layers Lots of Parameters Backpropagation
  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
  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
  37. Image Processing

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

  46. ImageNet Classification Error Over Time 0 5 10 15 20

    25 30 2010 2011 2012 2013 2014 2015 2016 Classification Error CNNs
  47. 2012 ImageNet Classification with Deep Convolutional Neural Networks

  48. SuperVision: 8 layers, 60M parameters 0

  49. 2013 Visualizing and Understanding Convolutional Networks

  50. None
  51. None
  52. http://www.asimovinstitute.org/neural-network-zoo/ Lots of Parameters Network Architectures defined by Hyperparameters Dropout

    Layers for Regularization
  53. Generative Adversarial Networks (GANs) Generator Neural Network Discriminator Neural Network

    Real or Generated? Real Picture Generated Picture
  54. 2016 Generative Adversarial Networks (GANs)

  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
  56. Artificial Intelligence on AWS P2, F1 & Elastic GPUs Deep

    Learning AMI and template Investment in Apache MXNet
  57. Apache MXNet

  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
  59. Amazon AI Bringing Powerful Artificial Intelligence To All Developers

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

    1
  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
  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
  63. None
  64. Deep Learning Process Conv 1 Conv 2 Conv n …

    … Feature Maps Labrador Dog Beach Outdoors Softmax Probability Fully Connected Layer
  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
  66. Amazon Polly Text To Speech Powered By Deep Learning 2

  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”
  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
  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
  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
  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
  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
  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
  74. Amazon ALEXA (It’s what’s inside Alexa) 3 Natural Language Understanding

    (NLU) & Automatic Speech Recognition (ASR) Powered By Deep Learning
  75. Amazon Lex: Speech Recognition & Natural Language Understanding Amazon Lex

    Automatic Speech Recognition Natural Language Understanding “What’s the weather forecast?” Weather Forecast
  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
  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
  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?”
  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.
  80. <demo> I See </demo>

  81. I see… Amazon Rekognition Amazon Polly Camera Raspberry Pi Voice

    Synthesize Speech Detect Labels Detect Faces
  82. None
  83. Nikola Tesla, 1926 “When wireless is perfectly applied, the whole

    earth will be converted into a huge brain…”
  84. Machine Learning For Developers Danilo Poccia @danilop danilop AWS Technical

    Evangelist