What we’re about
Hands on meetups (lectures, classes, demo talks) with free compute time sponsored by Amazon AWS. We focus on giving members the opportunity to learn and share with the rest of the programming community.
Upcoming events (4+)
See all- Reinforcement Learning Working GroupLink visible for attendees
RL Working Group
For new participants we will do an intro for those that show up and figure out logistics.
We are using the grokking RL from Miguel Morales and the Coursera RL courses.
Do the Coursera programming assignments and make your own demos and present in front of the group.
We will build on the demos from last week. Gridworld as a react app can be used for POCs and simulation to a non technical audience. We will build out the react app more to include probability distributions and comparison of value algorithms
RL: all of the fundamental RL concepts are required for any work with LLMs and Agents. The exploitation/exploration tradeoff is mentioned in the config.yaml files for OpenEvolve.
You work on your own projects and use the weekly meetups to motivate progress.Coursera RL: 4 classes, Fundamentals, Sampling, Approximation, Capstone. Start with this first.
Stanford cs234: the derivations are in the YT videos for this class.
You will need these for the derivations and proofs. Practice on the assignments on your own.
https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX
POMDPs and RL. Self driving cars, air control systems; most real world systems use POMDPs + RL. These are mentioned briefly in the grokking book but are explained in detail here: https://aa228v.stanford.edu/
The aa228v videos are on YT.How to build LLMs: https://stanford-cs336.github.io/spring2025/
This includes relevant information in 1 place with better detail than any blog post or YT video with starter code exercises. How to train foundation models, what are MOEs, fine tuning, benchmarking, etc...Manning RL Resources
https://www.manning.com/books/grokking-deep-reinforcement-learning - Group Study building LLMs at scale, Stanford cs336Needs location
Incredible material on LLMs. Quite possibly the best hands on class in the last 20y. Relevant to current job market with state of the art assignments. Riveting lectures.
First meetup: we are going to prequalify people and see if they are qualified to do the work. If nobody has pytorch experience we are going to cancel the remaining events.
The instructors have modified the course to make it open for self study.
All of the lectures are on YT
Course websiteThe assignments and pacing are quite difficult. Most of these are well beyond the range of the typical CS undergraduate.
The homeworks are substantial and require background and skills most people on meetup don't have so we are going to try something easier. You are free to form a group and do the hw assignments without looking at the solutions.
New format, going to try to review individual slides or demos as we review the cs336 LLM videos.
The format is participants view the lecture and come with a slide or 3 to discuss a particular topic or demo running code or progress towards the assignments.
As we progress over the course of 2 months we can separate people into working groups to make either summary slides to summarize key points or do code demos.
We can cover each lecture and see how far we get in 2months. These are the first 6 lectures.
cs336 Lecture 1 Tokenization
cs336 Lecture 2 Pytorch and Resource Accounting
cs336 Lecture 3 arch and hyperparams
cs336 lecture 4 MoE
cs336 Lecture 5 GPUs
cs336 Lecture 6 Kernels and Triton
