Generative AI Curriculum - Intermediate to Advanced

Course Curriculum: Generative AI

Course Level: Intermediate to Advanced

Duration: 12 Weeks

Format: Online/Offline, Hands-on Labs, Projects

Week 1-2: Introduction to Generative AI

• What is Generative AI?
• Definition and key concepts
• History and evolution
• Applications of Generative AI
• Text generation (ChatGPT, Bard, etc.)
• Image generation (DALL·E, MidJourney, Stable Diffusion)
• Music, video, and 3D content generation
• Mathematical Foundations
• Probability and statistics
• Linear algebra and matrix operations
• Neural networks and deep learning basics

Hands-on: Set up Python environment, install AI libraries (TensorFlow, PyTorch, Hugging Face, OpenAI API)

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Week 3-4: Deep Learning Fundamentals

• Neural Networks & Architectures
• CNNs, RNNs, Transformers
• Understanding Attention Mechanisms
• Training and Optimization
• Loss functions, backpropagation, and gradient descent
• Hyperparameter tuning
• Ethics & Bias in AI
• Fairness and bias mitigation
• Societal impacts and AI regulations

Hands-on: Train a simple neural network for text or image classification

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Week 5-6: Generative Models

• Introduction to Generative Models
• Autoencoders (AEs, VAEs)
• Generative Adversarial Networks (GANs)
• Transformers (GPT, BERT, T5)
• Variational Autoencoders (VAEs)
• Concept of latent space
• Applications in image and text synthesis

Hands-on: Implement a simple autoencoder for image reconstruction

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Week 7-8: Advanced Generative Models

• Generative Adversarial Networks (GANs)
• Architecture and working principle
• Types of GANs (DCGAN, CycleGAN, StyleGAN)
• Transformer-Based Models
• GPT architecture deep dive
• BERT vs GPT
• Fine-tuning transformers for NLP

Hands-on: Train a GAN to generate synthetic images

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Week 9-10: Multimodal AI & Fine-Tuning

• Multimodal Models
• CLIP, DALL·E, Stable Diffusion
• Combining text and image generation
• Fine-tuning Large Language Models (LLMs)
• Transfer learning strategies
• Dataset preparation and optimization

Hands-on: Fine-tune a GPT model on a custom dataset

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Week 11-12: Deployment & Real-World Applications

• Deploying Generative AI Models
• APIs, cloud deployment, and edge AI
• Model compression and efficiency
• Building AI Applications
• Chatbots, content creation tools, AI-generated media
• Industry use cases (healthcare, finance, gaming)
• Future Trends & Ethical Considerations

Final Project: Develop and deploy a generative AI application

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Prerequisites

• Python & basic programming
• Understanding of deep learning
• Familiarity with machine learning frameworks (TensorFlow/PyTorch)

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