The way AI-powered apps are built has changed:
Before LLMs, an idea would bottleneck on training models from scratch, and then it’d bottleneck again on scalable deployment. Now, a compelling MVP based on pre-trained LLM models and APIs can be configured and serve users in an hour. An entirely new ecosystem of techniques, tools, and tool vendors is forming around LLMs. Even ML veterans are scrambling to orient themselves to what is now possible and figure out the most productive techniques and tools.
- Learn best practices and tools for building LLM-powered apps
- Cover the full stack from prompt engineering to user-centered design
- Get up to speed on the state-of-the-art
The lectures aim to get anyone with experience programming in Python ready to start building applications that use LLMs.
Experience with at least one of machine learning, frontend, or backend will be very helpful.
Full Stack Deep Learning is a team of UC Berkeley PhD alumni with years of industry experience who are passionate about teaching people how to make deep neural networks work in the real world.
