In this section, we explore GANs, diffusers, and transformers for image and text generation, focusing on their architectures, applications, and comparative strengths in creative and technical domains.

In this section, we explore the evolution of natural language processing, focusing on the transformer architecture's role in modern large language models and generative AI. Key concepts include self-attention mechanisms, sequence-to-sequence learning, and deep learning foundations.

In this section, we explore transitioning generative AI from prototyping to production, focusing on setting up a Python environment, deploying pretrained LLMs, and ensuring scalable, reliable model deployment for real-world applications.

In this section, we explore fine-tuning generative models for task-specific applications like Q&A. Key concepts include parameter-efficient techniques and brand-aligned response generation.

In this section, we explore domain adaptation for LLMs, focusing on techniques like LoRA to enhance model understanding of specialized financial language and evaluate performance using ROUGE metrics.

In this section, we explore zero- and few-shot prompting, prompt-chaining, and RAG strategies to enhance LLM performance without fine-tuning, focusing on practical applications and accurate task execution.

In this section, we examine ethical norms, bias in generative AI, and strategies to minimize harm, emphasizing responsible development and trustworthy systems.