Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.

Understand and implement simple neural networks with forwarding pass and backward propagation using only two hidden layers.

• Linear data accuracy: 99%
• XOR data accuracy: 100%

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Implementing two EEG classification model which are EEGNet and DeepConvNet. Also, we are changing the activation function (ELU, ReLU, LeakyReLU) in the model and see the difference.

• EEGNet accuracy: 87.87%
• DeepConvNet accuracy: 76.48%

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Analyze diabetic retinopathy using ResNet, compare the results of ResNet18, ResNet50, and both networks with pre-trained weights.

• ResNet50 with pretrained accuracy: 82.18%
• ResNet18 with pretrained accuracy: 79.24%
• ResNet50 w/o pretrained accuracy: 73.55%
• ResNet18 w/o pretrained accuracy: 73.35%

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Implementing conditional seq2seq VAE for English tense conversion and generation.

monotonic KL annealing

• BLEU score: 0.8312
• Gaussian score: 0.472

cyclical KL annealing

• BLEU score: 0.9527
• Gaussian score: 0.452

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Implement conditional GAN to generate synthetic images in multi-label conditions.

• SAGAN acc: 77.22%
• cDCGAN acc: 68.89%
• WGAN acc: 60.56%

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Implement DQN and DDQN to solve LunarLander-v2. Implement DDPG to solve LunarLanderContinuous-v2.

• DQN average reward: 277
• DDPG average reward: 273
• DDQN average reward: 256

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