Generative Artificial Intelligence has revolutionized content creation by enabling intelligent systems to generate text, images, code, audio, and multimodal experiences with unprecedented quality. This course provides an enterprise-focused approach to developing, fine-tuning, optimizing, and deploying generative AI models for real-world business applications.
Students will explore the mathematical foundations of generative modeling before progressing through Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, Transformer architectures, Stable Diffusion, multimodal foundation models, prompt engineering, synthetic data generation, model fine-tuning, and production deployment.
Practical implementation includes PyTorch, Hugging Face Diffusers, Stable Diffusion XL, OpenAI-compatible APIs, LoRA fine-tuning, ControlNet, DreamBooth, ONNX Runtime, TensorRT, Docker, Kubernetes, and cloud-native AI services. By course completion, learners will be capable of building enterprise-grade generative AI platforms for creative automation, digital media, software development, healthcare, finance, and intelligent enterprise workflows.
Curriculum
- 1 Section
- 15 Lessons
- 7 Weeks
- Module: Generative AI Engineering: Foundation Models, Diffusion Networks & Multimodal Content GenerationDescription Understand the theoretical foundations of generative modeling, probability distributions, latent representations, autoregressive modeling, and enterprise applications of Generative Artificial Intelligence. Lessons Introduction to Generative AI Foundation Models Latent Space Representation Probability Distributions Generative Learning Paradigms Enterprise Use Cases15
- 1.1Lesson 1: Introduction to Generative AI
- 1.2Lesson 2: Mathematics for Generative AI
- 1.3Lesson 3: Foundation Models Explained
- 1.4Lesson 4: Transformer Architecture Deep Dive
- 1.5Lesson 5: Large Language Models (LLMs)
- 1.6Lesson 6: Prompt Engineering & In-Context Learning
- 1.7Lesson 7: Diffusion Models Fundamentals
- 1.8Lesson 8: Image Generation with Stable Diffusion
- 1.9Lesson 9: GANs vs Diffusion Models
- 1.10Lesson 10: Multimodal AI Systems
- 1.11Lesson 11: Retrieval-Augmented Generation (RAG)
- 1.12Lesson 12: Fine-Tuning Foundation Models
- 1.13Lesson 13: AI Agents & Autonomous Workflows
- 1.14Lesson 14: Model Evaluation & Safety
- 1.15Lesson 15: Capstone Project – Build a Multimodal Generative AI Application
Features
- Only a quid me old mucker squiffy tomfoolery
- Give us a bell bits and bobs Charles
- Gosh William ummm I'm telling crikey
Target audiences
- Business's managers, leaders
- Downloadable lectures, code and design assets for all projects
- Anyone who is finding a chance to get the promotion
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