The slides are mainly proving the viability of using AI in different domains, evidenced by Tsai-Min's probing in AI-related competitions from 8 different domains within 2018.
23. 痞客邦
黑客松
MolHac
kII線上
黑客松
人工智
慧大賽
中國生
理訊號
賽
生技醫
療創新
黑客松
全國智
慧製造
大數據
創造勝
利Fund
程式
農業創
新黑松
23
TM-NN
Input(None, 256000, 1)
Dense
Output(None, 3)
Bidirectional RNN
Convolution
Convolution-Pooling
Convolution
X5
CNN Block
Attention
1. Combination of CNN and bidirectional RNN with
Attention layer.
2. 5 CNN-blocks including 2 convolution layers that
follow the convolution-pooling layer
3. Dropout was driven by randomly dropping 20% of the
connections to the next block or layer.
4. In the last CNN-block, we connected it into a
bidirectional RNN with Attention layer, and applied the
batch normalization before it was connected to the
fully-connected layer.
5. LeakyReLU activation function was used for each
layer, except for the last fully-connected layer, where
Sigmoid activation function was used.
6. Model was trained with categorical-cross-entropy
loss function and ADAM optimizer.
7. Newly released data was applied as Validation Set to
optimize the accuracy.