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Deep Generative Models for Trading and Asset Management
Curriculum
8 Sections
50 Lessons
10 Weeks
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Chapter 4: Understanding Generative AI
7
1.1
4.1 Why Generative Models
10 mins
1.2
4.2 Difference with Discriminative Models
10 mins
1.3
4.3 How Can We Use Them?
10 mins
1.4
4.4 Illustrating Generative Models with ChatGPT
10 mins
1.5
4.5 Hybrid Modeling: Combining Generative and Discriminative Models
10 mins
1.6
4.6 Taxonomy of Generative Models
10 mins
1.7
4.7 Conclusion
10 mins
Chapter 5: Deep Autoregressive Models for Sequence Modeling
5
2.1
5.1 Representation Complexity
10 mins
2.2
5.2 Representation and Complexity Reduction
10 mins
2.3
5.3 A Short Tour of Key Model Families
10 mins
2.4
5.4 Model Fitting
10 mins
2.5
5.5 Conclusions
10 mins
Chapter 6: Deep Latent Variable Models
7
3.1
6.1 Introduction
10 mins
3.2
6.2 Latent Variable Models
10 mins
3.3
6.3 Examples of Traditional Latent Variable Models
10 mins
3.4
6.4 Learning
10 mins
3.5
6.5 Variational Autoencoder (VAE)
10 mins
3.6
6.6 VAEs for Sequential Data and Time Series
10 mins
3.7
6.7 Conclusion
10 mins
Chapter 7: Flow Models
9
4.1
7.1 Introduction
10 mins
4.2
7.2 Model Training
10 mins
4.3
7.3 Linear Flows
10 mins
4.4
7.4 Designing Nonlinear Flows
10 mins
4.5
7.5 Coupling Flows
10 mins
4.6
7.6 Autoregressive Flows
10 mins
4.7
7.7 Continuous Normalizing Flows
10 mins
4.8
7.8 Modeling Financial Time Series with Flow Models
10 mins
4.9
7.9 Conclusion
10 mins
Chapter 8: Generative Adversarial Networks
7
5.1
8.1 Introduction
10 mins
5.2
8.2 Training
10 mins
5.3
8.3 Some Theoretical Insight in GANs
10 mins
5.4
8.4 Why Is GAN Training Hard? Improving GAN Training Techniques
10 mins
5.5
8.5 Wasserstein GAN (WGAN)
10 mins
5.6
8.6 Extending GANs for Time Series
10 mins
5.7
8.7 Conclusion
10 mins
Chapter 9: Leveraging LLMs for Sentiment Analysis in Trading
6
6.1
9.1 Sentiment Analysis in Fed Press Conference Speeches Using Large Language Models
10 mins
6.2
9.2 Data: Video + Market Prices
10 mins
6.3
9.3 Speech-to-text Conversion
10 mins
6.4
9.4 Sentiment Analysis
10 mins
6.5
9.5 Experiment Results
10 mins
6.6
9.6 Conclusion
10 mins
Chapter 10: Efficient Inference
6
7.1
10.1 Introduction
10 mins
7.2
10.2 Scaling Large Language Models: High Performance, High Computational Cost, and Emergent Abilities
10 mins
7.3
10.3 Making FinBERT Faster
10 mins
7.4
10.4 Model Quantization
10 mins
7.5
10.5 Customizing Your LLM: Adapting Models to Your Needs
10 mins
7.6
10.6 Conclusions
10 mins
Chapter 11: Afterword
3
8.1
11.1 Diffusion Models
10 mins
8.2
11.2 Combining Generative Model Variants
10 mins
8.3
11.3 LLMs as Financial Advisors
10 mins
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