About LearnLLMFast Team

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.
• Build the mathematical depth for LLM research. Connect probability, vectors, optimization, attention, loss functions, and model behavior deeply enough to progress into papers, advanced courses, open-source model work, and specialized research topics.
• Explore scaling laws and emergent capabilities. Understand how data, parameters, and compute interact as models scale—and why larger training runs can unlock surprisingly different capabilities.
• Understand how modern LLM reasoning works. Explore chain-of-thought, inference-time computation, search, verification, tool-assisted reasoning, and the techniques used to make models spend more computation on difficult problems.
• Write prompts like an expert. Learn how instructions, context, examples, constraints, roles, structured outputs, and generation settings combine to control model behavior deliberately.
• Bring private knowledge into LLM applications. Use retrieval, context engineering, enterprise documents, policies, databases, and web search to give models the information they need at inference time.
• Design self-improving AI loops. Combine generation, evaluation, feedback, revision, verification, and repeated execution so LLM systems can refine outputs and improve performance over multiple passes.
• Turn model output into reliable data. Use structured generation, schemas, validation, synthetic labeling, filtering, and distillation workflows to turn probabilistic model output into useful data for software and downstream models.
• Understand LLM security from the model to the agent. Learn how prompt injection, data leakage, poisoned retrieval, tool permissions, jailbreaks, and agent boundaries create new security problems around language-model systems.
• Work with models that understand more than text. Explore multimodal LLMs that combine language with images, audio, video, documents, and other signals—and understand how those modalities become part of one model.