JasonYao81000 / MLDS2018SPRING

Machine Learning and having it Deep and Structured (MLDS) in 2018 spring

MLDS2018SPRING

Machine Learning and having it deep and structured (MLDS) at NTU 2018 Spring.

This course has four homeworks, group by group. The four homeworks are as follows:

1. Deep Learning Theory
2. Sequence-to-sequence Model
3. Deep Generative Model
4. Deep Reinforcement Learning

Browse this course website for more details.

Table of Contents

1. Deep Learning Theory
   • Deep vs Shallow
   • Optimization
   • Generalization
2. Sequence-to-sequence Model
   • Video caption generation
   • Chat-bot
3. Deep Generative Model
   • Image Generation
   • Text-to-Image Generation
   • Style Transfer
4. Deep Reinforcement Learning
   • Policy Gradient
   • Deep Q Learning
   • Actor-Critic

Results of Four Homeworks

1. Deep Learning Theory

• Report.pdf

1.1 Deep vs Shallow

• README

1.2 Optimization

• README

1.3 Generalization

• README

2. Sequence-to-sequence Model

2.1 Video caption generation

• BLEU@1 = 0.7204
• README
• hw2_1/report.pdf

2.2 Chat-bot

• Perplexity = 11.83, Correlation Score = 0.53626
• README
• hw2_2/report.pdf

3. Deep Generative Model

• report.pdf

3.1 Image Generation

• README
• Image Generation: 100% (25/25) Pass Baseline

./gan-baseline/baseline_result_gan.png

3.2 Text-to-Image Generation

• README
• Text-to-Image Generation: 100% (25/25) Pass Baseline

Testing Tags	./gan-baseline/baseline_result_cgan.png
blue hair blue eyes blue hair green eyes blue hair red eyes green hair blue eyes green hair red eyes

3.3 Style Transfer

• README

4. Deep Reinforcement Learning

• report.pdf

4.1 Policy Gradient

• README
• Policy Gradient: Mean Rewards in 30 Episodes = 16.466666666666665

4.2 Deep Q Learning

• README
• Deep Q Learning: Mean Rewards in 100 Episodes = 73.16

4.3 Actor-Critic

• README