- 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
Course Introduction
In our last course, "Generative AI Fundamentals," we looked at the key principles and theories that underpin Generative AI. Students gained knowledge of the many varieties of generative models, such as autoregressive models, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). To further comprehend how these models produce fresh data, we delved into foundational mathematics, including distributions of probabilities and neural network topologies. Students were also taught fundamental ideas like latent spaces, sampling techniques, and the training procedures that enable generative models to acquire knowledge from data.
Our focus in this intermediate/advanced course will shift from theory to practice. Using well-known frameworks like TensorFlow and PyTorch, students will acquire practical knowledge in creating and optimizing generative models. Let’s begin.
Hello and welcome to "Generative AI: Intermediate and Advanced." With an emphasis on advanced approaches and practical application, this course will take you beyond the foundations of Generative AI. In this course, you will learn how to use frameworks like as TensorFlow and PyTorch to construct, optimize, and evaluate generative models. Complex generating problems can be tackled with the help of sophisticated approaches like GANs, VAEs, StyleGAN, and CycleGAN. Important subjects such as data quality evaluation, model optimization, and transfer learning will also be covered.
You should come into this course with a strong grasp of neural networks, Python, and fundamental generative AI ideas at the very least. This course will teach you how to build complex generative models, improve their performance via tuning, and then use those models to solve real-world issues. You will also learn how to assess and enhance the quality of produced data, setting you up to be an innovator in artificial intelligence.
Course Outline and Goals:
- Develop and improve GANs to generate images and text.
- Make VAEs better at creating data and putting it back together.
- Learn about state-of-the-art generating methods such as StyleGAN and CycleGAN.
- Make use of transfer learning to enhance model efficiency while reducing data requirements.
- Improve the precision and efficiency of generative models by optimizing and debugging them.
- Evaluate the produced data for its quality using several measures.
Prerequisites:
- Grasping the fundamentals of neural networks and generative AI.
- Mastery of the Python programming language.
- You will leave with the ability to build sophisticated generative models in your hands.
- Capacity to use these ideas to address practical issues.
- Competence in assessing and enhancing the quality of generative data.
Students will leave this course with the knowledge and abilities necessary to develop advanced generative AI models, train them to solve real-world issues using these models, and ultimately become the next generation of AI innovators.
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