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Deep Learning on AWS with Gabe Hollombe

Deep Learning on AWS with Gabe Hollombe

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AWS User Group Mumbai

March 13, 2019
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  1. © 2018, Amazon Web Services, Inc. or its Affiliates. All

    rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Gabe Hollombe, Sr. Technical Evangelist @gabehollombe A Gentle Intro to Deep Learning Or, “I can Deep Learning and So Can You” February 2019
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    rights reserved. Gabe Hollombe Software Developer & Technical Evangelist, Amazon Web Services 15+ years writing all sorts of software EdTech ⇢ Agile/XP Consulting ⇢ Nano-satellites ⇢ AWS Web / JS / Ruby / Python / Clojure / C# Bad puns, fresh sushi, old whiskey @gabehollombe ! " ❤
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    rights reserved. What we’ll cover 1. Kick off a demo (put the cookies in the oven) 2. What’s Deep Learning? What’s a Neural Network? 3. Look at some code 4. Finish the demo (enjoy our cookies) 5. How to do this yourself 6. Where to learn more
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    rights reserved. What is Deep Learning?
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    rights reserved. Machine Learning A technique to help computers learn how to do things that are easy for humans (but hard to explicitly program). Self-driving Cars Sentiment Analysis Object Detection Facial Recognition
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    rights reserved. Machine Learning Using neural networks with multiple layers, which allows computers to learn from complex data without needing to explicitly define the features of the data. Neural Networks Deep Learning Computation inspired by how our brains work. Proven to be capable of performing any computation, given enough memory. Computers figuring out how to do things without being explicitly programmed.
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    rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark You don’t need a PHD. You don’t need a ton of data. You can start using deep learning for your own projects today. Without a dedicated data science team of experts.
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    rights reserved. { "Labels": [ { "Name": "Skateboard", "Confidence": 99.25341796875 }, { "Name": "Sport", "Confidence": 99.25341796875 }, { "Name": "Sports", "Confidence": 99.25341796875 }, { "Name": "Human", "Confidence": 99.24723052978516 },
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    rights reserved. We can build and deploy a custom image classifier. Right now.
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    rights reserved. First, let’s collect some images of things we want to classify…
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    rights reserved. • Put something in the oven • Understand the concepts • Read the recipe • Check in on the baking • Taste test
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    rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark What is a Neural Network? By Egm4313.s12 (Prof. Loc Vu-Quoc) - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=72816083
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    rights reserved.
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    rights reserved. Node with a value Neural Network recap linked to other nodes with various connection strengths. Each node value is based on how much of each connecting node’s signal arrives into it, plus some other value to decide what the final value will be. “Neuron” “Weights” “Bias” 2 3 1 5 -4 (1 * 2) + (5 * 3) + -4 = 13 13 Example – Two inputs into a neuron
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    rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark How does a Neural Network learn?
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    rights reserved. Keep trying random connection strengths between all of the neurons until we find a combo that works? No!
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    rights reserved.
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    rights reserved. 0. Randomly initialize the weights and biases (only once at the start). 1. Send each training example through the network, look at the output nodes, and measure the difference between what you wanted to see versus what you actually got. 2. Go backwards through the network, adjust the weights and biases by tiny amounts, trying to nudge the output values closer to what you want to see. 3. Repeat until the network performs well for all of your examples. 4. Run some validation examples through the network to make sure it works for new data, too. Training recap
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    rights reserved. Image Classification trick: examine neighboring pixels together
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    rights reserved. Image Kernels Explained Visually http://setosa.io/ev/image-kernels/
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    rights reserved. Layer 1 Contrasts & Similarity Layer 2 Lines, Circles, Gradients Layer 3 Patterns, Text, Human Shapes Layer 5 Dog heads, Bike wheels, Animal eyes, Flower centers . . . via https://arxiv.org/abs/1311.2901 Visualizing and Understanding Convolutional Networks
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    rights reserved. Making A Custom Image Classifier In 21 Lines of Code
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    rights reserved. FRAMEWORKS AND INTERFACES ML for data scientists KERAS Frameworks Inter faces APPLICATION SERVICES ML for everyone PLATFORM SERVICES ML for engineers NVIDIA Tesla V100 GPUs (14x faster than P2) Machine Learning AMIs INFRASTRUCTURE Powering the ML Intel Xeon Skylake (Optimized for ML) AWS GREENGRASS ML L E X P O L L Y R E K O G N I T I O N I M A G E & V I D E O T R A N S C R I B E T R A N S L A T E C O M P R E H E N D F O R E C A S T P E R S O N A L I Z E AMAZON SAGEMAKER AWS DEEPLENS SAGEMAKER GROUND TRUTH & MECHANICAL TURK SPARK & EMR
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    rights reserved. Amazon SageMaker Build Train Deploy • Managed notebooks for authoring models • Templates for common ML applications • Built-in, high performance algorithms • Broad framework support • One-click training • Automatic model tuning • One-click deployment • Automatic A/B testing • Fully-managed hosting with auto scaling
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    rights reserved. This is what we ran in the Jupyter Notebook on AWS
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    rights reserved. Let’s try out our image classifier!
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    rights reserved. More ML is built on AWS than anywhere else
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    rights reserved. Detecting the best images to use • Expedia have over 10 million images from 300,000 hotels • Using great images boosts conversion • They fine-tuned a pre-trained Convolutional Neural Network using 100,000 images • Hotel descriptions now automatically feature the best available images https://news.developer.nvidia.com/expedia-ranking-hotel-images- with-deep-learning/
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    rights reserved. Improving written text with Amazon SageMaker “Amazon SageMaker makes it possible for us to develop our TensorFlow models in a distributed training environment. (…) We can run inference on SageMaker itself, or if we need just the model, we download it from S3 and run inference of our mobile device implementations for iOS and Android customers.”
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    rights reserved. Where can I learn more?
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    rights reserved. Great places to get started with Deep Learning 3Blue1Brown’s YouTube series on Neural Networks ~ 60 Minutes https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi Fast.ai’s Practical Deep Learning for Coders ~ 14 Hours http://www.fast.ai/ Neural Networks and Deep Learning, by Michael Neilsen ~ 6 Chapter Book http://neuralnetworksanddeeplearning.com/ Amazon SageMaker - Fully-managed Platform https://aws.amazon.com/sagemaker/ These Demos - Jupyter Notebooks & Web Apps https://github.com/gabehollombe-aws/jupyter-notebooks https://github.com/gabehollombe-aws/webcam-s3-uploader https://github.com/gabehollombe-aws/webcam-sagemaker-inference
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    rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Machine Learning is for Everyone.
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    rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark What will you build?
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    rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Thank you! Please keep in touch: ! @gabehollombe ✉ gabehol@amazon.com