Generative AI
Generative AI Course
Day 1-5: Introduction to Generative AI and Foundations
Day 1: Introduction to AI and Generative AI
- Overview of AI and Machine Learning
- What is Generative AI?
- Applications of Generative AI
- Key differences between Discriminative and Generative models
- Overview of GANs, VAEs, and Transformer-based models
Day 2: Basics of Deep Learning
- Introduction to Neural Networks
- Perceptron and Feedforward Networks
- Activation Functions (ReLU, Sigmoid, Tanh)
- Loss functions and Optimization (Gradient Descent)
- Backpropagation and training process
Day 3: Neural Network Architectures
- Multilayer Perceptron (MLP)
- CNNs: Convolutional Neural Networks
- RNNs: Recurrent Neural Networks
- LSTMs and GRUs (for sequence data)
- Intro to Autoencoders
Day 4: Introduction to Generative Models
- Overview of Generative Models
- Probabilistic vs. Deterministic Models
- Importance of Likelihood, Distribution, and Sampling
- Variational Inference and Maximum Likelihood Estimation
Day 5: Mathematical Foundation for Generative AI
- Probability theory basics
- Bayes’ Theorem
- Latent Variable Models
- Entropy, KL Divergence, and Jensen’s Inequality
- Basic linear algebra (Matrix operations, Eigenvalues, Singular Value Decomposition)
Day 6-10: Generative Models Overview
Day 6: Generative Adversarial Networks (GANs) - Part 1
- What are GANs?
- Architecture: Generator and Discriminator
- The adversarial training process
- Loss functions in GANs
- First introduction to simple GANs
Day 7: Generative Adversarial Networks (GANs) - Part 2
- Training challenges (Mode collapse, Non-convergence)
- Techniques to stabilize training (WGAN, LSGAN)
- Applications of GANs (image generation, text-to-image, etc.)
- Hands-on with a simple GAN in PyTorch or TensorFlow
Day 8: Variational Autoencoders (VAEs)
- What are VAEs?
- Architecture and components of VAE
- Encoder-Decoder architecture
- Variational Inference in VAEs
- Differences between GANs and VAEs
Day 9: VAE Hands-on
- Implementing a VAE in TensorFlow/PyTorch
- Generating new data with VAE
- Visualizing latent space
- Comparing VAEs with GANs
Day 10: Autoencoders and Representation Learning
- Basic Autoencoders and their variants
- Denoising Autoencoders
- Applications in image compression and anomaly detection
- Evaluation metrics for generative models
Day 11-15: Advanced Generative Models
Day 11: Deep Convolutional GANs (DCGANs)
- Introduction to Convolutional Neural Networks (CNNs) in GANs
- DCGAN architecture
- Advantages and challenges of DCGANs
- Applications of DCGANs in image generation
Day 12: Conditional GANs (cGANs)
- What are conditional GANs?
- Conditioning on labels or additional information
- Applications (image generation from text, image-to-image translation)
- Hands-on with cGAN implementation
Day 13: CycleGANs and Pix2Pix
- Introduction to CycleGAN
- Image-to-image translation without paired data
- Pix2Pix: Conditional GAN for paired image translation
- Hands-on: Build a CycleGAN for style transfer or domain adaptation
Day 14: Attention Mechanisms and Transformers
- Introduction to Attention Mechanisms
- Self-attention and its applications in Generative AI
- Overview of Transformer architecture
- Generative Pre-trained Transformers (GPT)
Day 15: GPT Models and Text Generation
- Transformer models and language modeling
- Introduction to GPT models
- Pre-training and Fine-tuning strategies for GPT
- Applications of GPT for text generation
Day 16-20: Text and Multimodal Generative Models
Day 16: Text Generation with GPT-2 and GPT-3
- Overview of GPT-2 and GPT-3
- Pre-training and transfer learning
- Fine-tuning models for specific tasks
- Hands-on: Build a text generator using GPT-2/3
Day 17: Multimodal Generative Models
- Combining text and images (CLIP, DALL·E)
- Multi-modal embeddings and cross-modal retrieval
- Generative models for audio-visual synthesis
- Practical use cases (Text-to-image generation)
Day 18: DALL·E: Generating Images from Text Descriptions
- Overview of DALL·E model
- Text-to-image generation using deep learning
- Applications in creative industries and design
- Hands-on: Implement a simple text-to-image model
Day 19: Music and Audio Generation with AI
- Introduction to music generation models (WaveNet, MuseNet)
- Autoencoders and GANs for audio synthesis
- Generating realistic sound effects and voices
- Hands-on: Build a simple music generation model
Day 20: Style Transfer and Neural Artistic Rendering
- Neural Style Transfer (NST) technique
- Applications of style transfer in art and media
- Generative models for artistic rendering
- Hands-on: Implement Neural Style Transfer
Day 21-25: Advanced Applications of Generative Models
Day 21: Deepfakes and Ethical Concerns
- What are Deepfakes?
- GANs in Deepfake generation
- Ethical considerations and societal impact of deepfakes
- Defending against deepfakes: detection methods
Day 22: Generative Models for Data Augmentation
- Using GANs for augmenting training datasets
- Application in NLP and computer vision
- Improving model generalization through augmentation
- Hands-on: Generate synthetic data using GANs for ML tasks
Day 23: Reinforcement Learning and Generative AI
- Combining Generative Models with Reinforcement Learning
- Generative models for policy generation
- Applications in robotics and game AI
- Hands-on: Reinforcement learning with generative models
Day 24: GANs in Healthcare
- Applications of GANs in medical imaging
- Generating synthetic medical data for training models
- Enhancing privacy with synthetic medical data
- Hands-on: Medical image generation using GANs
Day 25: Ethical AI and Bias in Generative Models
- Ethical issues in AI and generative models
- Biases in generative models and solutions
- Ensuring fairness in generative AI applications
- Exploring regulatory frameworks and standards
Day 26-30: Advanced Training and Optimization
Day 26: Advanced GAN Techniques
- Wasserstein GANs (WGAN)
- Least Squares GANs (LSGAN)
- Improved Training Techniques for GANs
- Hands-on with advanced GAN techniques
Day 27: Model Evaluation and Metrics
- Evaluation metrics for generative models (IS, FID)
- Visual quality assessment
- Measuring model diversity and novelty
- Practical: Evaluating a generative model’s output
Day 28: Scaling Generative Models
- Scaling models to large datasets
- Training on GPUs/TPUs, distributed training
- Optimizing for inference speed and quality
- Hands-on: Scaling a model for real-world applications
Day 29: Transfer Learning in Generative AI
- Overview of transfer learning for generative models
- Pre-training large models and fine-tuning them
- Transfer learning in multimodal tasks
- Hands-on: Transfer learning for text-to-image models
Day 30: Hyperparameter Tuning for Generative Models
- Importance of hyperparameter tuning
- Techniques for tuning GANs and VAEs
- Grid search and random search
- Hands-on: Hyperparameter optimization with grid search
Day 31-40: Specialized Topics in Generative AI
Day 31: GANs for 3D Model Generation
- 3D data representation and generation with GANs
- Applications in gaming and VR
- Hands-on: Generating 3D models with GANs
Day 32: Generating Structured Data
- Use cases for generating structured data (tabular, time-series)
- Generative models for business intelligence
- Hands-on: Generate structured data with GANs or VAEs
Day 33: Graph-based Generative Models
- Graph neural networks (GNNs) for generative tasks
- Applications in drug discovery and social networks
- Hands-on: Graph generation with GANs
Day 34: Adversarial Attacks and Defenses in Generative AI
- Overview of adversarial attacks
- Defending against adversarial attacks on generative models
- Techniques like adversarial training and regularization
- Hands-on: Defending against adversarial samples
Day 35: Generative AI in Creative Arts and Design
- Applications in music, visual arts, and literature
- AI as a co-creator in art and media industries
- Case studies of generative AI in creative industries
- Hands-on: AI-generated artwork or music project
Day 41-50: Project Work and Real-World Applications
Day 41-45: Real-World Project Work
- Assigning a group project or individual task
- Choose between various application areas (text-to-image, music, healthcare, etc.)
- Gathering datasets and preparing training environments
- Weekly progress check-ins and mentorship
Day 46-50: Fine-tuning and Optimization
- Refining models based on feedback
- Performance evaluation and fine-tuning
- Troubleshooting common model issues
- Preparing models for deployment
Day 51-60: Capstone Projects and Advanced Topics
Day 51-55: Capstone Project Development
- Developing final project based on earlier concepts
- Implementing generative models in real-world contexts
- Using datasets, fine-tuning, and performance evaluation
Day 56-59: Final Presentation and Project Review
- Presenting the Capstone Project
- Peer reviews and constructive feedback
- Model evaluation and improvement suggestions
Day 60: Conclusion and Next Steps in Generative AI
- Reviewing course content
- Future trends in Generative AI (GPT-4, 5, and beyond)
- Ethical implications and AI governance
- Further study resources and career pathways