- 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
Recurrent Neural Networks (RNNs) and LSTMs
Introduction to Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs) are a type of neural network that is intended to work with sequential data. Conventional Neural Networks (CNNs) are best at processing spatial data like pictures. Recurrent Neural Networks (RNNs), on the other hand, are great at processing timed events like time series, natural language, and speech. RNNs can keep a secret state that stores information about earlier time steps. This is what makes them able to behave in a way that changes over time. This hidden state is changed every time step depending on the input and the hidden state before it. This makes RNNs very good at jobs where past or context is important. But RNNs have a problem called "vanishing gradient" when they deal with long sequences. This makes it harder for them to find long-term relationships. Advanced types like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) deal with this problem.
ChatGPT
Python
PowerShell
PL/SQL
Spring Boot
CSS
