Learn what happens between a prompt and a dependable result.

Joe Chang

Joe Chang brings more than 20 years of software engineering experience across startups, research, international banking and FinTech, entrepreneurship, and production systems. A physics graduate of New York University who learned deep learning from Yann LeCun, he has taught more than 1,000 students across machine learning, deep learning, computer vision, reinforcement learning, and large language models. He has also worked with universities and government programs on AI engineering education.

• Understand modern model architectures. Learn how attention, transformers, dense and sparse computation, mixture-of-experts designs, and other architectural ideas shape capability, efficiency, and model behavior.
• Follow the full LLM training pipeline. Connect pretraining, supervised fine-tuning, preference learning, reinforcement-learning fine-tuning, alignment, and post-training into one picture of how modern models are created.
• Build agentic AI that can plan and act. Learn how LLMs choose tools, decompose goals, maintain state, make decisions, and coordinate multi-step work across an execution loop.
• Position yourself for high-paying AI work. Build knowledge that carries into LLM engineering, AI research, model training, application development, technical interviews, and the rapidly expanding market for people who understand modern AI deeply.
• Stay close to the cutting edge of AI. Learn the ideas behind new model architectures, training methods, reasoning techniques, and research directions while the field is still moving quickly.