Generative AI Bootcamp
Hands-on training in building and deploying Generative AI models.
Duration
8 Weeks
Level
Intermediate
About this Course
This intensive bootcamp provides hands-on experience with Generative AI models like GANs, VAEs, and Transformers. Learn to build, train, and deploy models for image generation, text synthesis, and more, using frameworks like TensorFlow and PyTorch.
What You'll Learn
- Understand the core concepts and architectures of various Generative AI models.
- Implement and train GANs, VAEs, and Transformer-based models.
- Generate realistic images, text, and other data using AI.
- Evaluate and troubleshoot generative models.
- Deploy generative AI models into production environments.
- Address ethical considerations in generative AI development.
Course Modules
Module 1: Introduction to Generative AI
- What is Generative AI?
- Distinction from Discriminative Models
- Applications of Generative AI (Art, Text, Data Augmentation)
- Ethical Considerations in Generative AI
Module 2: Variational Autoencoders (VAEs)
- Introduction to Autoencoders
- Latent Space and Variational Inference
- Building and Training VAEs with TensorFlow/PyTorch
- Image Generation with VAEs
Module 3: Generative Adversarial Networks (GANs)
- GAN Architecture (Generator, Discriminator)
- Training Dynamics and Challenges (Mode Collapse)
- Different GAN Architectures (DCGAN, WGAN, CycleGAN)
- Image Synthesis and Style Transfer with GANs
Module 4: Transformer Models for Text Generation
- Introduction to Transformers (Attention Mechanism)
- Encoder-Decoder Architecture
- GPT (Generative Pre-trained Transformer) models
- Fine-tuning Transformers for specific tasks
- Text Generation and Summarization
Module 5: Diffusion Models
- Introduction to Diffusion Probabilistic Models
- Denoising Diffusion Probabilistic Models (DDPMs)
- Applications in Image and Audio Generation
- Comparison with GANs and VAEs
Module 6: Deployment & Productionizing Generative Models
- Model Optimization and Quantization
- Deployment Strategies (APIs, Serverless)
- Monitoring and Maintaining Generative Models
- Ethical Deployment and Responsible AI Practices
Module 7: Advanced Topics & Project Work
- Conditional Generative Models
- Few-shot and Zero-shot Learning in Generative AI
- Latest Research Trends
- Capstone Project: Build and deploy a generative AI application.
Course Overview
Prerequisites
- Strong Python programming skills, basic understanding of machine learning.
Target Audience
- AI/ML Engineers
- Data Scientists
- Researchers interested in Generative AI
- Developers looking to integrate generative capabilities into applications
Technologies Covered
TensorFlowPyTorchGANsVAEsTransformersPython