Mastering Generative AI – From Foundations to Advanced Applications

 

This course provides a comprehensive understanding of Generative AI, covering fundamentals, model architectures, training techniques, ethical considerations, and real-world applications. It is designed for beginners and professionals who want to build, fine-tune, and deploy generative AI models.

Module 1: Introduction to Generative AI
What is Generative AI?
History & Evolution of Generative Models
Applications of Generative AI (Text, Images, Code, Music, Videos)
Open Source vs. Closed Source AI Models

Module 2: Fundamentals of Deep Learning for Generative AI
Neural Networks: Basics of ANN, CNN, and RNN
Transformers: Self-Attention, Encoder-Decoder Architecture
Understanding Large Language Models (LLMs)
Introduction to PyTorch and TensorFlow

Module 3: Generative Models & Architectures
Autoencoders (AE) and Variational Autoencoders (VAE)
Dimensionality Reduction & Latent Space Representations
Generative Adversarial Networks (GANs)
How GANs Work: Generator & Discriminator
Variants of GANs (DCGAN, StyleGAN, CycleGAN, etc.)
Transformer-Based Models
GPT (Generative Pre-trained Transformers)
BERT vs. GPT: Key Differences
LLaMA, Falcon, Mistral, and other Open Source AI Models
Diffusion Models (Stable Diffusion, DALL·E, Midjourney, etc.)
Image Generation Using Diffusion Techniques

Module 4: Training and Fine-Tuning Generative Models
Data Collection & Preprocessing for AI Models
Transfer Learning and Fine-Tuning Pretrained Models
Reinforcement Learning with Human Feedback (RLHF)
Parameter Optimization: Learning Rates, Batch Sizes & Regularization

Module 5: Implementing Generative AI in Real-World Applications
Text Generation – Building AI Chatbots, Story Generators
Image Generation – Creating AI Art, Deepfakes, and Image Editing
Video & Music Generation – AI in Animation & Music Composition
Code Generation – Using AI for Software Development (GitHub Copilot, Code Llama)

Module 6: Ethical Considerations & AI Governance
Bias & Fairness in AI Models
Security & Misuse of Generative AI
Copyright & Intellectual Property Challenges
AI Regulations & Responsible AI Practices

Module 7: Deploying Generative AI Models
Converting Models to APIs for Production
Using Cloud Services (AWS, Google Cloud, Azure) for AI Model Deployment
Optimization Techniques for Efficient AI Model Serving

Final Project & Certification
Hands-on Capstone Project (Build a GPT-like Model or AI Art Generator)
Course Completion Certificate

Who Should Take This Course?
✅ AI Enthusiasts & Developers
✅ Data Scientists & Machine Learning Engineers
✅ Digital Artists & Content Creators
✅ Business Leaders Exploring AI Solutions

📍 Course Mode: Online & Offline (Hyderabad)
📅 Duration: 3-6 Months
🔗 Visit for More Details & Enrollment: www.syntaxminds.com