Applied Deep Learning (2019 Spring) @ NTU
This course is lectured by Yun-Nung (Vivian) Chen and has four homeworks. The four homeworks are as follows:
- Dialogue Modeling
- Contextual Embeddings
- Deep Reinforcement Learning
- Conditional Generative Adversarial Nets
Browse this course website for more details.
- Dialogue Modeling
- Data Preprocessing
- Training and Prediction
- Results (Recall@10)
- Sequence Classification with Contextual Embeddings
- Part 1. Train an ELMo to beat the simple baseline
- Part 2. Beat the strong baseline with nearly no limitation
- Deep Reinforcement Learning
- Policy Gradient
- Deep Q-Learning (DQN)
- Actor-Critic
- Conditional Generative Adversarial Nets
- Cartoon Set
- Evaluation
- Train Condiction GANs
- Training Tips for Improvement
- Evaluate Condiction GANs
- FID Scores
- Training Progress
- Loss and Accuracy
- Human Evaluation Results
Results of Four Homeworks
3. Deep Reinforcement Learning
3.2. Deep Q-Learning (DQN)
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4. Conditional Generative Adversarial Nets
- Resnet-based ACGAN with BCE loss (resnet_1000)
4.2. Human Evaluation Results
- Resnet-based ACGAN with BCE loss (resnet_1000)