- 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
Understanding the Architecture and Functioning of VAEs
Variational Autoencoders (VAEs) are strong generative models that are made to figure out how a dataset's probabilities are distributed. There are two major parts that make up a VAE's architecture: the encoder and the decoder. The decoder shrinks the input data into a hidden space with fewer dimensions, keeping only the most important parts of the data. The decoder then uses this hidden version to put together the raw data. This process helps the model learn a short and useful way to show the data, which can then be used to make new samples that are similar. During training, VAEs improve two types of loss: the reconstruction loss, which makes sure that the input data is correctly rebuilt; and the Kullback-Leibler (KL) divergence, which limits the latent space in a way that is based on probability and encourages smooth and continuous data generation.
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