- Introduction and Setup
- Course Introduction
- Environment Setup
- Advanced Neural Network Architectures
- Advanced Convolutional Neural Networks (CNNs)
- Implementing ResNet for Image Classification Tasks from Scratch
- Recurrent Neural Networks (RNNs) and LSTMs
- Understanding RNNs
- Code Sample for RNN
- Understanding LSTMs
- Code Sample for LSTM
- Understanding GRUs
- Code Sample for GRU
- Generative Adversarial Networks (GANs)
- Understanding the Architecture of GANs
- The Role of the Generator and Discriminator
- Implementing a Simple GAN for Image Generation
- Advanced GAN Techniques
- Implementing a Conditional GAN for MNIST Digit Generation
- Wasserstein GANs (WGANs)
- Implementing a WGAN for MNIST Digit Generation
- CycleGANs for image translation
- Implementing a CycleGAN for Image Translation
- Variational Autoencoders (VAEs)
- Understanding the Architecture and Functioning of VAEs
- The Concept of Latent Space and Reconstruction
- Practical Coding: Implementing VAEs
- Enhancing the VAE
- Transformer Models
- Architecture of the Transformer model
- The Attention Mechanism and Its Importance
- Practical Coding: Implementing Transformers
- Application: Implementing a Transformer for text generation
- Advanced Applications of Generative Models
- Fine-tuning GPT-3 for Custom Text Generation Tasks
- Enhancing Fine-Tuning with More Training Data
- Implementing neural style transfer
- Using GANs for High-Resolution Image Synthesis
- Music and Audio Generation
- Optimization and Fine-Tuning
- Techniques for Optimizing Model Performance
- Using Grid Search and Random Search for Hyperparameter Tuning
- Transfer Learning with BERT Model
- Real-World Projects
- Generating Realistic Faces with GANs
- Building a Chatbot with Transformer Models
- Best Practices and Advanced Tips
- Model Evaluation and Validation
- Measurements of Quantitative Importance for Text Generation
- Deploying Generative Models
- Future Trends and Research Directions
- Emerging Trends in Generative AI
- Ethical Considerations
- Conclusion
Wasserstein GANs (WGANs)
Wasserstein GANs (WGANs)
Wasserstein Generative Adversarial Networks (WGANs) are an improved version of the classic GAN architecture developed by Martin Arjovsky, Soumith Chintala, and Léon Bottou. While standard GANs have problems like unreliable training and mode collapse, WGANs fix some of the most important ones. An important difference between WGANs and standard GANs is that WGANs use the Wasserstein distance, which is also called the Earth Mover's distance. More stable training and better convergence qualities come from this distance measure, which gives an optimization gradient that is smoother and more meaningful.
Making the Wasserstein distance between the real data distribution and the produced data distribution as small as possible is the main idea behind WGANs. With this method, the discriminator in a normal GAN is switched out for a reviewer who rates the "realness" of data samples instead of just labeling them as real or fake. Following this method helps the model learn a better way to describe the data, which leads to more accurate samples being made.
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