Machine Learning for Developers - Danilo Poccia - Codemotion Rome 2017
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The document provides an overview of machine learning, detailing various types such as supervised, unsupervised, and reinforcement learning, alongside historical advancements and the introduction of neural networks. It highlights applications of machine learning in services like Amazon Rekognition and Polly, which utilize deep learning for image recognition and text-to-speech capabilities respectively. The document also emphasizes the importance of model training, validation, and real-world use cases in diverse fields, from marketing automation to missing person identification.
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Machine Learning for Developers - Danilo Poccia - Codemotion Rome 2017
- 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/
- 6. Model
- 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
- 27. Neural Networks
- 28. 1943 Warren McCulloch, Walter Pitts Threshold Logic Units
- 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 2016Classification 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%. WORDSPHONEMES { { { { { ˈtwɛn.ti pɚ.ˈsɛnt ˈmɑɹ.kət ˈgɹu baɪ ˈmoʊɹ ˈðæn PROSODY CONTOURUNIT SELECTION AND ADAPTATION TEXT PROCESSING PROSODY MODIFICATIONSTREAMING 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.
- 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