Learn AI and Large Language Models from theory to practice
Overview, history, and scope of AI
Core algorithms for finding solutions in AI.
How AI systems store, use, and infer knowledge.
Core concepts of learning from data.
Introduction to neural networks and their components.
Core techniques for teaching computers to understand text.
Defining LLMs, their evolution, and capabilities.
A deep dive into the components of the Transformer model.
How text is converted into a format LLMs can understand.
Understanding the massive undertaking of pre-training a large language model.
Adapting pre-trained LLMs for specific downstream tasks.
Aligning LLMs with human values and ensuring safe outputs.
The practical challenges of deploying LLMs in the real world.
Addressing the societal impact of AI and looking ahead.