Generative AI and Large Language Models
Description
Comprehensive understanding of generative artificial intelligence, large language models, transformer architectures, and generative AI applications across text, image, and video domains.
Learning Objectives
Learners will master generative AI concepts including transformer models, large language models (LLMs), diffusion models, generative adversarial networks, and multimodal AI systems. They will understand the lifecycle of generative AI development, training processes, and applications in content creation, automation, and decision support.
Topics (9)
Comprehensive introduction to generative AI concepts, generative vs discriminative models, and the evolution of generative technologies.
Detailed study of transformer models, attention mechanisms, encoder-decoder architectures, and their revolutionary impact on AI.
In-depth exploration of LLM architectures, training methodologies, parameter scaling, emergent capabilities, and model families.
Comprehensive study of foundation models, pre-training objectives, self-supervised learning, and transfer learning principles.
Comprehensive coverage of image generation models, diffusion processes, DALL-E, Midjourney, and Stable Diffusion architectures.
Study of video generation techniques, audio synthesis models, text-to-speech systems, and multimodal content creation.
Comprehensive study of word embeddings, sentence embeddings, multimodal embeddings, and vector databases for AI applications.
End-to-end study of generative AI project lifecycle, deployment strategies, monitoring, and maintenance of generative AI systems.
Detailed exploration of text generation methods, autoregressive models, language modeling objectives, and text-based applications.