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|Webinars© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Sunil Mallya & Yash Pant, Amazon AI
Sep...
|Webinars
• Intro to AI and Deep Learning
• Intro to GANs (Generative Adversarial Networks)
• GAN Research & Applications
...
|Webinars
Tons of GPUs and CPUs
Serverless
At the Edge, On IoT Devices
Prediction
The Challenge For Artificial Intelligenc...
|Webinars
Amazon AI
|Webinars
0.2
-0.1
...
0.7
Input Output
1 1 1
1 0 1
0 0 0
3
mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,...
|Webinars
Artificial Neuron
output
synaptic
weights
input
Input
Vector of training data x
Output
Linear functions of input...
|Webinars
Deep Neural Network
hidden layers
The optimal size of the hidden
layer (number of neurons) is
usually between th...
|Webinars
The “Learning” in Deep Learning
0.4 0.3
0.2 0.9
...
back propogation (gradient descent)
X1 != X
0.4 ± 𝛿 0.3 ± 𝛿
...
|Webinars
Gradient Descent
|Webinars
Convolution Neural Network (CNN)
CNN Layers
Convolutional Layer
Pooling Layer
Activation
Fully-Connected Layer
|Webinars
GAN (Generative Adversarial Networks)
Input:
Noise
Generator
Generated
(“Fake”)
Data
Data From
Dataset
(“Real”)
...
|Webinars
Image Generation
Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (R...
|Webinars
Image Arithmetic
Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (R...
|Webinars
SRGAN: Making Images High Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversaria...
|Webinars
StackGAN: Create Images from Text
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adve...
|Webinars
3D-GAN: 3D Models from Images
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversari...
|Webinars
CycleGAN: Image to Image Translation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Netw...
|Webinars
DiscoGAN: Discover Relationships Between Images
Learning to Discover Cross-Domain Relations with Generative Adve...
|Webinars
Implementation: DCGAN
(Deep Convolutional Generative Adversarial Network)
Goal: Create a model that is able to g...
|Webinars
DCGAN: Concept Overview
[
.76
.14
.83
-.06
]
Z
“Fake” Image
“Real” Image
“Fake” Image
|Webinars
DCGAN: Implementation Walkthrough
Code available at:
https://github.com/yash1/mxnet-
notebooks/blob/master/pytho...
|Webinars
AWS Deep Learning AMI: One-Click Deep Learning
Kepler, Volta
& Skylake
Apache MXNet Python 2/3 Notebooks
& Examp...
|Webinars
Thank you!
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Introduction to Generative Adversarial Networks (GAN) with Apache MXNet

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GANs are a type of deep neural network that allow us to generate data. In this webinar, we’ll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We’ll explore the GAN framework along with its components -- generator and discriminator networks. We’ll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we’ll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.

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Introduction to Generative Adversarial Networks (GAN) with Apache MXNet

  1. 1. |Webinars© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Sunil Mallya & Yash Pant, Amazon AI September 2017 Introduction to Generative Adversarial Networks using Apache MXNet
  2. 2. |Webinars • Intro to AI and Deep Learning • Intro to GANs (Generative Adversarial Networks) • GAN Research & Applications • GAN Implementation & Demo Agenda
  3. 3. |Webinars Tons of GPUs and CPUs Serverless At the Edge, On IoT Devices Prediction The Challenge For Artificial Intelligence: SCALE Tons of GPUs Elastic capacity Training Pre-built images Aggressive migration New data created on AWS Data PBs of existing data
  4. 4. |Webinars Amazon AI
  5. 5. |Webinars 0.2 -0.1 ... 0.7 Input Output 1 1 1 1 0 1 0 0 0 3 mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2) lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed) 4 2 2 0 4=Max 1 3 ... 4 0.2 -0.1 ... 0.7 mx.sym.FullyConnected(data, num_hidden=128) 2 mx.symbol.Embedding(data, input_dim, output_dim = k) Queen 4 2 2 0 2=Avg Input Weights cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman) mx.sym.Activation(data, act_type="xxxx") "relu" "tanh" "sigmoid" "softrelu" Neural Art Face Search Image Segmentation Image Caption “People Riding Bikes” Bicycle, People, Road, Sport Image Labels Image Video Speech Text “People Riding Bikes” Machine Translation “Οι άνθρωποι ιππασίας ποδήλατα” Events mx.model.FeedForward model.fit mx.sym.SoftmaxOutput Anatomy of a Deep Learning Model mx.sym.Convolution(data, kernel=(5,5), num_filter=20) Deep Learning Models
  6. 6. |Webinars Artificial Neuron output synaptic weights input Input Vector of training data x Output Linear functions of inputs Nonlinearity Transform output into desired range of values, e.g. for classification we need probabilities [0, 1] Training Learn the weights w and bias b
  7. 7. |Webinars Deep Neural Network hidden layers The optimal size of the hidden layer (number of neurons) is usually between the size of the input and size of the output layers Input layer output
  8. 8. |Webinars The “Learning” in Deep Learning 0.4 0.3 0.2 0.9 ... back propogation (gradient descent) X1 != X 0.4 ± 𝛿 0.3 ± 𝛿 new weights new weights 0 1 0 1 1 . . - - X input label ... X1
  9. 9. |Webinars Gradient Descent
  10. 10. |Webinars Convolution Neural Network (CNN) CNN Layers Convolutional Layer Pooling Layer Activation Fully-Connected Layer
  11. 11. |Webinars GAN (Generative Adversarial Networks) Input: Noise Generator Generated (“Fake”) Data Data From Dataset (“Real”) Discriminator Real or Fake? Framework for creating generative models
  12. 12. |Webinars Image Generation Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (Radford et. al 2016)
  13. 13. |Webinars Image Arithmetic Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks (Radford et. al 2016)
  14. 14. |Webinars SRGAN: Making Images High Resolution Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (Ledig et. al 2016 )
  15. 15. |Webinars StackGAN: Create Images from Text StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks (Zhang et. al 2016)
  16. 16. |Webinars 3D-GAN: 3D Models from Images Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (Wu et al 2017)
  17. 17. |Webinars CycleGAN: Image to Image Translation Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (Zhu et al 2017)
  18. 18. |Webinars DiscoGAN: Discover Relationships Between Images Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (Kim et al 2017) Gender conversion Hair color conversion
  19. 19. |Webinars Implementation: DCGAN (Deep Convolutional Generative Adversarial Network) Goal: Create a model that is able to generate realistic looking images from a given dataset MNIST Dataset
  20. 20. |Webinars DCGAN: Concept Overview [ .76 .14 .83 -.06 ] Z “Fake” Image “Real” Image “Fake” Image
  21. 21. |Webinars DCGAN: Implementation Walkthrough Code available at: https://github.com/yash1/mxnet- notebooks/blob/master/python/tutorials/dcgan_create_images.ipynb
  22. 22. |Webinars AWS Deep Learning AMI: One-Click Deep Learning Kepler, Volta & Skylake Apache MXNet Python 2/3 Notebooks & ExamplesTensorflow, Keras, Caffe2.. https://aws.amazon.com/amazon-ai/amis/
  23. 23. |Webinars Thank you!

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