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
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
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
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
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