简体中文
EnglishWhat is The Full Stack (fullstackdeeplearning.com)?
Updated
The Full Stack is a hub for news, community, and courses aimed at people building AI-powered products. Its stated premise is that shipping an AI product involves much more than training a model or writing a prompt — the site covers the whole lifecycle, from defining the problem and choosing a GPU or foundation model through production deployment, continual learning, and user experience design. It is a good fit if you want free, structured course material on applied AI engineering rather than research papers or a single-model tutorial.
What the site actually contains
The homepage organizes its material into three course tracks plus a blog and news section:
| Section | What it covers | Notable items |
|---|---|---|
| LLM Bootcamp (Spring 2023) | Foundations of LLM-powered apps | "Launch an LLM App in One Hour," LLM Foundations, Prompt Engineering, Augmented Language Models, a project walkthrough (askFSDL), UX for language UIs, LLMOps, "What's Next?" |
| Deep Learning Course (FSDL 2022) | Building an AI application from the ground up | Lectures on course vision and when to use ML, development infrastructure and tooling, troubleshooting and testing, data management, deployment, continual learning, foundation models, ML teams and project management, ethics |
| Older courses (FSDL 2021, 2021 Berkeley, 2020 UW, 2019, 2018) | Earlier iterations of the same curriculum | Deep learning fundamentals, CNNs, RNNs, transformers, MLOps infrastructure, data labeling, testing and CI, web deployment, research directions, panels such as "Do I need a PhD to work in ML?" |
The 2022 and 2023 tracks pair lectures with labs — for example, experiment management, data annotation, web deployment, and model monitoring — so the material is oriented toward doing the work, not just reading about it.
Who it is for
The site describes its audience as people building AI-powered products, and explicitly frames the scope as broader than model training or prompt writing. In practice that means:
- Engineers moving from a model or prototype to a shipped product, who need deployment, monitoring, and continual learning practices.
- Teams deciding how to structure an ML project, including when to use ML at all and how to manage data and experiments.
- People working on LLM applications, who want the 2023 bootcamp's coverage of prompting, augmented language models, and LLMOps.
- Those weighing a career or team question, served by lectures on ML teams, project management, and startups.
It is less suited to someone looking for a single framework's API reference or a pure deep-learning theory course.
How to use it
- Pick the track that matches your stage: the 2023 LLM Bootcamp for LLM apps, the 2022 Deep Learning Course for end-to-end product building, or the older courses for fundamentals.
- Work through lectures in order and do the associated labs, since the labs are where tooling and troubleshooting practices are exercised.
- Use the blog and news section for updates — for example, the "Multiplayer AI Manifesto" post — and the community as a place to compare practices.
Practical notes
The site states that at least one course is shared for free ("We are excited to share this course with you for free"), and the homepage surfaces a "subscribe" option, which indicates some form of ongoing updates or membership. Pricing details beyond that are not specified on the page, so treat cost and access terms as something to confirm on the site itself rather than assuming everything is free or unrestricted.