How to Access The Full Stack's News, Community, and Course Content

The Full Stack (fullstackdeeplearning.com) is a site for people building AI-powered products, offering news, community, and courses. You can reach its main content through the top-level navigation: Home, Working with AI Agents, LLM Bootcamp, Deep Learning Course, Blog, and Cloud GPUs. Course materials are organized by year and program, and at least some content is explicitly offered for free — the site states, "We are excited to share this course with you for free." A subscribe option also exists, but the available evidence doesn't specify what it includes or whether it costs anything.

Site Sections and What Each One Holds

The site frames its scope broadly: "Building an AI-powered product is much more than just training a model or writing a prompt." It covers everything from defining the problem and picking a GPU or foundation model through production deployment, continual learning, and UX design.

LLM Bootcamp (Spring 2023)

This is the entry point if you want to explore the foundations of LLM-powered apps. The listed sessions are:

• Launch an LLM App in One Hour
• LLM Foundations
• Learn to Spell: Prompt Engineering
• Augmented Language Models
• Project Walkthrough: askFSDL
• UX for Language User Interfaces
• LLMOps
• What's Next?
• Reza Shabani: How to train your own LLM
• Harrison Chase: Agents
• Fireside Chat with Peter Welinder

The sequence moves from a fast first build through foundations, prompting, and tooling, then into operations and guest talks. If you're new to LLM apps, "Launch an LLM App in One Hour" followed by "LLM Foundations" is the natural starting pair.

Deep Learning Course (FSDL 2022)

The 2022 course pairs lectures with labs, so you can read a concept and then practice it. The lecture/lab structure is:

• Lecture 1: Course Vision and When to Use ML — Lab Overview
• Lecture 2: Development Infrastructure & Tooling — Lab 4: Experiment Management
• Lecture 3: Troubleshooting & Testing — Lab 5: Troubleshooting & Testing
• Lecture 4: Data Management — Lab 6: Data Annotation
• Lecture 5: Deployment — Lab 7: Web Deployment
• Lecture 6: Continual Learning — Lab 8: Model Monitoring
• Lecture 7: Foundation Models
• Lecture 8: ML Teams and Project Management
• Lecture 9: Ethics
• Project Showcase

Note that the lab numbering doesn't line up one-to-one with lecture numbering in the listing, so treat the labs as a separate track rather than assuming Lab 4 belongs only to Lecture 2.

Older Course Archives

If you want earlier or more foundational material, the site keeps prior years online:

• FSDL 2021 — a synchronous online course with lectures on DL fundamentals, CNNs, RNNs, transformers, ML projects, MLOps infrastructure, troubleshooting, data management, AI ethics, testing and explainability, deployment and monitoring, research directions, and ML teams and startups. It also includes a panel discussion, "Do I need a PhD to work in ML?", and a course projects showcase.
• FSDL 2021 (Berkeley), FSDL 2020 (UW), FSDL 2019 (Online), FSDL 2019 (Bootcamp), and FSDL 2018 (Bootcamp) are also listed.

The 2021 labs cover setup and introduction, coding a neural net, CNNs and synthetic data, RNNs, transformers, experiment management, data labeling, paragraph recognition, testing and CI, and web deployment.

News and Community

The site describes itself as offering "news, community, and courses for people building AI-powered products," and the Home page says The Full Stack "brings people together to learn and share best practices." The Blog is where news-style posts appear — for example, "Introducing the Multiplayer AI Manifesto." A subscribe option is present on the site; the available evidence doesn't say what subscribing gets you or whether it involves payment, so check the subscribe flow directly if that matters to you.

How to Decide Where to Start

• New to LLM apps: Start with the LLM Bootcamp, beginning with "Launch an LLM App in One Hour."
• Want the full product lifecycle: Work through the FSDL 2022 Deep Learning Course lectures and labs.
• Need fundamentals first: Use the FSDL 2021 archive, which covers CNNs, RNNs, and transformers before moving to MLOps and deployment.
• Just want updates: Check the Blog and the subscribe option.

One practical note: the site states that at least one course is shared for free, but free access isn't confirmed for every section. Verify on the specific course page before assuming a given program is free.