This document summarizes a presentation about MXNet, an open source deep learning framework. It discusses using MXNet for linear regression, logistic regression, deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). It also covers running MXNet on Amazon Web Services (AWS) using the Deep Learning AMI and GPU instances for efficient training of deep learning models.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Sung Kim, Xingjian Shi
홍콩과기대 (HKUST)
모두를 위한 MXNET

2. 본 강연에서 다룰 내용
●MXNET
●Linear Regression, Logistic Regression
●Deep Neural Net (DNN)
●CNN
●RNN
●MXNET in AWS

3. ● Open Source deep learning framework
● Most scalable and flexible framework
● Partner with AWS
http://www.allthingsdistributed.com/2016/11/mxnet-default-framework-deep-learning-aws.html

4. https://aws.amazon.com/mxnet/
Why MXNET?

5. http://mxnet.io/architecture/program_model.html

6. Imperative vs Symbolic Programs
● Imperative-style programs perform computation as you run them
● In symbolic programs, when program is executed, no computation
occurs.
● Instead, these operations generate a computation graph
http://mxnet.io/architecture/program_model.html

7. Imperative Programs Are More Flexible
● Imperative-style programs perform computation as you run them
http://mxnet.io/architecture/program_model.html

8. Symbolic Programs Are More Efficient
http://mxnet.io/architecture/program_model.html

9. Running code in GPU
http://mxnet.io/architecture/program_model.html

10. NN Layers

11. (Linear regression) Hypothesis
x=2.5, y=?

12. (Linear regression) Hypothesis

13. Simplified hypothesis

14. Logistic Regression
http://cs231n.github.io/neural-networks-1/

15. Logistic Regression

16. Logistic regression
https://nbviewer.jupyter.org/github/sxjscience/DeepLearningZeroToAll/blob/master/aws-2017-seoul/mxnet-logistic_regression_diabetes.ipynb

17. Forward Neural Net
http://cs231n.github.io/neural-networks-1/

18. Forward NN Code (Demo)

19. CNN
http://parse.ele.tue.nl/cluster/2/CNNArchitecture.jpg

20. Convolution layer and max pooling

21. Simple convolution layer
Image: 1,3,3,1 image, Filter: 2,2,1,1, Stride: 1x1, Padding: VALID
1
1
1
1
[[[[1.]],[[1.]]],
[[[1.]],[[1.]]]]
shape=(2,2,1,1)

22. Max Pooling

23. Deep CNN
Image credit: http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/index.html

24. CNN

25. Inception-V3

26. RNN
1) Using native implementation
rnn = mx.rnn.LSTMCell (num_hidden=2, prefix="lstm1_")
1) Using CUDNN V5.1
rnn = mx.rnn.FusedRNNCell (num_hidden=2, prefix="lstm1_")

27. Unfolding to n sequences
Hidden_size=2
sequence_length=5

28. Sequence to sequence model

29. Sequence to Sequence

30. MXNET on AWS
Instance: g2.2x-large (K520)
Task: CIFAR-10 with ResNet-50
AWS Deep Learning AMI
Up to~40k CUDA cores
Apache MXNet
TensorFlow /Theano / Caffe
Torch / Keras
Pre-configured CUDA drivers,
MKL
Anaconda, Python3
Ubuntu and Amazon Linux
One-Click GPU or CPU
Deep Learning
+ CloudFormation template
+ Container Image

31. Summary
● MXNET
● Linear Regression, Logistic Regression
● Deep Neural Net (DNN)
● CNN
● RNN
● MXNET in AWS

32. 본 강연이 끝난 후…
MXNet Resources:
• MXNet Blog Post | AWS Endorsement
• Read up on MXNet and Learn More: mxnet.io
• MXNet Github Repo
• MXNet Recommender Systems Talk | Leo Dirac
Developer Resources:
• Deep Learning AMI |Amazon Linux
• Deep Learning AMI | Ubuntu
• CloudFormation Template Instructions
• Deep Learning Benchmark
• MXNet on Lambda
• MXNet on ECS/Docker
• MXNet on Raspberry Pi | Image Detector using Inception Network

33. Thank you!
함께 해주셔서 감사합니다!

34. https://www.awssummit.kr
AWS Summit 모바일 앱을 통해 지금 세션 평가에
참여하시면, 행사 후 기념품을 드립니다.
#AWSSummitKR 해시태그로 소셜 미디어에 여러분
의 행사 소감을 올려주세요.
발표 자료 및 녹화 동영상은 AWS Korea 공식 소셜 채널
로 공유될 예정입니다.
여러분의 피드백을 기다립니다!