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모두를 위한 MxNET - AWS Summit Seoul 2017

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모두를 위한 MxNET - AWS Summit Seoul 2017

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Sung Kim, Xingjian Shi 홍콩과기대 (HKUST) 모두를 위한 MXNET
  2. 2. 본 강연에서 다룰 내용 ●MXNET ●Linear Regression, Logistic Regression ●Deep Neural Net (DNN) ●CNN ●RNN ●MXNET in AWS
  3. 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. 4. https://aws.amazon.com/mxnet/ Why MXNET?
  5. 5. http://mxnet.io/architecture/program_model.html
  6. 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. 7. Imperative Programs Are More Flexible ● Imperative-style programs perform computation as you run them http://mxnet.io/architecture/program_model.html
  8. 8. Symbolic Programs Are More Efficient http://mxnet.io/architecture/program_model.html
  9. 9. Running code in GPU http://mxnet.io/architecture/program_model.html
  10. 10. NN Layers
  11. 11. (Linear regression) Hypothesis x=2.5, y=?
  12. 12. (Linear regression) Hypothesis
  13. 13. Simplified hypothesis
  14. 14. Logistic Regression http://cs231n.github.io/neural-networks-1/
  15. 15. Logistic Regression
  16. 16. Logistic regression https://nbviewer.jupyter.org/github/sxjscience/DeepLearningZeroToAll/blob/master/aws-2017-seoul/mxnet-logistic_regression_diabetes.ipynb
  17. 17. Forward Neural Net http://cs231n.github.io/neural-networks-1/
  18. 18. Forward NN Code (Demo)
  19. 19. CNN http://parse.ele.tue.nl/cluster/2/CNNArchitecture.jpg
  20. 20. Convolution layer and max pooling
  21. 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. 22. Max Pooling
  23. 23. Deep CNN Image credit: http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/index.html
  24. 24. CNN
  25. 25. Inception-V3
  26. 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. 27. Unfolding to n sequences Hidden_size=2 sequence_length=5
  28. 28. Sequence to sequence model
  29. 29. Sequence to Sequence
  30. 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. 31. Summary ● MXNET ● Linear Regression, Logistic Regression ● Deep Neural Net (DNN) ● CNN ● RNN ● MXNET in AWS
  32. 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. 33. Thank you! 함께 해주셔서 감사합니다!
  34. 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!

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