A Gentle Intro to Deep Learning

© 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

© 2018, Amazon Web Services, Inc. or its Affiliates. All 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
!
"
❤

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

What is Deep Learning?

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

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.

© 2018, Amazon Web Services, Inc. or its Affiliates. All 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.

{ "Labels": [
{ "Name": "Skateboard", "Confidence": 99.25341796875 },
{ "Name": "Sport", "Confidence": 99.25341796875 },
{ "Name": "Sports", "Confidence": 99.25341796875 },
{ "Name": "Human", "Confidence": 99.24723052978516 },

We can build and deploy
a custom image classifier.
Right now.

First, let’s collect some images
of things we want to classify…

• Put something in the oven
• Understand the concepts
• Read the recipe
• Check in on the baking
• Taste test

© 2018, Amazon Web Services, Inc. or its Affiliates. All 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

© 2018, Amazon Web Services, Inc. or its Aﬃliates. All 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

© 2018, Amazon Web Services, Inc. or its Aﬃliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
How does a Neural Network learn?

Keep trying random connection
strengths between all of the
neurons until we ﬁnd a combo
that works?
No!

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

Image Classiﬁcation trick: examine neighboring pixels together

Image Kernels
Explained Visually
http://setosa.io/ev/image-kernels/

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

Making A Custom Image Classifier
In 21 Lines of Code

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

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

This is what we ran
in the Jupyter Notebook
on AWS

Let’s try out our image classifier!

More ML is built on AWS than anywhere else

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/

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.”

Where can I learn more?

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

Machine Learning is for Everyone.

What will you build?

Thank you!
Please keep in touch:
! @gabehollombe
✉ gabehol@amazon.com

A Gentle Intro to Deep Learning

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