Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible, and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexible, and massively scalable open source deep learning framework that supports innovative deep models, including convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). This session will show you how to use the AWS Deep Learning AMI and Cloud Formation template to deploy and train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy as a practical introduction to Apache MXNet.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Guy Ernest | Developer Advocate | Amazon AI
June 21 2017
Deep Dive on Deep Learning with Apache
MXNet

2. Significantly improve many applications on multiple
domains
“deep learning” trend in the past 10 years
image understanding speech recognition natural language
processing
…
Deep Learning
autonomy

3. 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

4. Any Data Any Problem

8. MNIST dataset in t-SNE

9. MNIST Notebook

10. Gradient Descent

11. MNIST Notebook – CNN filter

15. MNIST Notebook – Activation Options

19. LSTM Notebook

20. RNN Examples
Image Caption Sentiment Analysis Machine Translation Video LabelingImage Labeling

26. MF Example - Embedding

27. MF Example - Optimizers

28. SDG Visualization
Source: https://twitter.com/alecrad

29. DSSM Example - Recommender

30. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.