In recent months, Artificial Intelligence has become the hottest topic in the IT industry. In this session, we’ll explain how Deep Learning — a subset of AI — differs from traditional Machine Learning, and how AWS customers use it to solve complex problems such as computer vision and natural language processing. Then, using Python code, we’ll show you how to start writing and training models using Apache MXNet, a popular library for Deep Learning which supports both CPUs and GPUs. Finally, we’ll demonstrate how you can achieve excellent results quickly by using state-of-the-art, pre-trained models.

1. Deep Dive on Apache
MXNet with Python
Julien Simon, AI Evangelist, EMEA
@julsimon

2. What to expect
• An introduction to Deep Learning
• An introduction to Apache MXNet
• Demos using Jupyter notebooks on Amazon SageMaker
• Resources

3. • Artificial Intelligence: design software applications which exhibit
human-like behavior, e.g. speech, natural language processing,
reasoning or intuition
• Machine Learning: teach machines to learn from data without
being explicitly programmed
• Deep Learning: using neural networks, teach machines to learn
from data where features cannot be explicitly expressed

4. Amazon Echo
Amazon Alexa is based on Deep Learning

5. Amazon AI is based on Deep Learning

6. https://www.oreilly.com/ideas/self-driving-trucks-enter-the-fast-lane-using-deep-learning
Last June, tuSimple drove an autonomous truck
for 200 miles from Yuma, AZ to San Diego, CA

7. An introduction to Deep Learning

8. Activation functions
The neuron
! xi ∗ wi
&
'()
= u
”Multiply and Accumulate”
Source: Wikipedia

9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
x =
x11, x12, …. x1I
x21, x22, …. x2I
… … …
xm1, xm2, …. xmI
I features
m samples
y =
y1
y2
…
ym
m labels,
N2 outputs
0,0,1,0,0,…,0
1,0,0,0,0,…,0
…
0,0,0,0,1,…,0
One-hot encoding
Neural networks

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
x =
x11, x12, …. x1I
x21, x22, …. x2I
… … …
xm1, xm2, …. xmI
I features
m samples
y =
y1
y2
…
ym
m labels,
N2 categories
Total number of predictions
Accuracy =
Number of correct predictions
0,0,1,0,0,…,0
1,0,0,0,0,…,0
…
0,0,0,0,1,…,0
One-hot encoding
Neural networks

11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Neural networks
Initially, the network will not predict correctly
f(X1) = Y’1
A loss function measures the difference between
the real label Y1 and the predicted label Y’1
error = loss(Y1, Y’1)
For a batch of samples:
! loss(Yi, Y’i)
,-./0 2345
3()
= batch error
The purpose of the training process is to
minimize error by gradually adjusting weights

12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Training
Training data set Training
Trained
neural network
Batch size
Learning rate
Number of epochs
Hyper parameters
Backpropagation

13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Validation
Validation data set Trained
neural network
Validation
accuracy
Prediction at
the end of
each epoch
Save the model at the end of each epoch

14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Early stopping
Training accuracy
Loss function
Accuracy
100%
Epochs
Validation accuracy
Loss
Best checkpoint
OVERFITTING

15. Apache MXNet: Open Source library for Deep Learning
Programmable Portable High Performance
Near linear scaling
across hundreds of
GPUs
Highly efficient
models for
mobile
and IoT
Simple syntax,
multiple
languages
Most Open Best On AWS
Optimized for
Deep Learning on AWS
Accepted into the
Apache Incubator

16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The Apache MXNet API
• Storing and accessing data in multi-dimensional arrays
àNDArray API
• Building models (layers, weights, activation functions)
à Symbol API
• Serving data during training and validation
à Iterators
• Training and using models
à Module API

17. Demos
https://github.com/juliensimon/dlnotebooks
1) Synthetic data set
2) Classify images with pre-trained models
3) Learn MNIST with a Multi-Layer Perceptron
4) Learn MNIST with the LeNet CNN
5) Predict handmade MNIST samples
6) Train and host a MNIST model with Amazon SageMaker

18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Resources
https://aws.amazon.com/machine-learning
https://aws.amazon.com/blogs/ai
https://mxnet.incubator.apache.org
https://github.com/apache/incubator-mxnet
https://github.com/gluon-api
https://github.com/awslabs/sockeye
https://medium.com/@julsimon

19. Thank you!
Julien Simon, AI Evangelist, EMEA
@julsimon