- 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
Code Sample for LSTM
Code Sample for LSTM:
| import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense # Create an LSTM model model = Sequential() model.add(LSTM(50, input_shape=(None, 1), return_sequences=True)) model.add(Dense(1)) # Compile the model model.compile(optimizer='adam', loss='mse') # Summary of the model model.summary() |
In this code, an LSTM model for sequence prediction is set up. With 50 units and return_sequences=True, the LSTM layer shows the secret state at each time step. Mean squared error loss and the Adam planner are used to put together the model.
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