What is Generative AI?

• Definition and significance
• Historical background and evolution

Types of Generative AI Models

• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)
• Transformers

Applications of Generative AI

• Text generation
• Image synthesis
• Audio and music generation
• Other innovative uses

Introduction to Artificial Intelligence

• Understanding AI and its subfields
• Difference between AI, ML, and Deep Learning

Basics of Machine Learning

• Supervised vs. Unsupervised Learning
• Common algorithms and their applications

Getting Started with Python

• Python basics: Data types, control structures, functions
• Python libraries for ML: NumPy, pandas, scikit-learn

Introduction to Neural Networks

• Structure and function of neural networks
• Activation functions, loss functions, and optimization

Deep Learning Frameworks

• Overview of TensorFlow and PyTorch
• Setting up the development environment

Building Neural Networks

• Creating and training simple neural networks
• Evaluating model performance

Understanding VAEs

• Basic concepts and architecture
• Differences between VAEs and traditional autoencoders

Building VAEs

• Implementing VAEs with TensorFlow/PyTorch
• Training and evaluating VAEs

Applications of VAEs

• Image reconstruction
• Anomaly detection

Introduction to GANs

• Concept of adversarial training
• Architecture of GANs: Generator and Discriminator

Implementing GANs

• Building and training GANs with TensorFlow/PyTorch
• Techniques to stabilize GAN training

Advanced GAN Variants

• DCGAN, StyleGAN, CycleGAN
• Use cases and applications

Understanding Transformers

• Concept of self-attention
• Transformer architecture

Implementing Transformers

• Building and training Transformers for NLP tasks
• Overview of popular Transformer models: BERT, GPT

Applications of Transformers

• Text generation
• Language translation
• Text summarization

Techniques for Text Generation

• Markov Chains
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) networks

Implementing Text Generation Models

• Building models with RNNs and LSTMs
• Fine-tuning pre-trained models like GPT-3

 Applications of Text Generation

• Chatbots
• Creative writing
• Code generation

Techniques for Image Generation

• Convolutional Neural Networks (CNNs)
• Deep Convolutional GANs (DCGANs)

Implementing Image Generation Models

• Building models with CNNs and DCGANs
• Fine-tuning pre-trained models like StyleGAN

Applications of Image Generation

• Art creation
• Image editing
• Synthetic data generation

Techniques for Audio Generation

• WaveNet
• Recurrent Neural Networks for audio

Implementing Audio Generation Models

• Building models for speech synthesis
• Music composition with neural networks

Applications of Audio Generation

• Voice assistants
• Music production
• Sound design

Overview of Tools and Platforms

• TensorFlow, PyTorch, Hugging Face
• OpenAI’s GPT-3, DeepMind’s AlphaFold

Cloud Platforms for Generative AI

• AWS SageMaker
• Google Cloud AI Platform
• Azure Machine Learning

Integrating Tools with Cloud Platforms

• Deploying models on cloud
• Scaling generative AI applications

Ethical Implications of Generative AI

• Bias and fairness
• Deepfakes and misinformation

Ensuring Responsible Use

• Guidelines for ethical AI development
• Regulatory and compliance considerations

Case Studies and Best Practices

• Analyzing real-world implications
• Implementing ethical AI policies

Industry Applications of Generative AI

• Healthcare, finance, entertainment, and more
• Case studies of successful implementations

Capstone Project

• Defining a real-world project
• Building, deploying, and showcasing a generative AI model

Advanced Generative AI Techniques

• Federated learning
• Transfer learning in generative models

Future Trends in Generative AI

• Emerging technologies
• Predictions for the future of generative AI

Preparing for Continued Learning

• Resources for ongoing education
• Building a professional network

Final Review and Certification

• Course recap and key takeaways
• Certification exam and project presentation
• Awarding of certificate of completion
• Networking and community building opportunities