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
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. They implement PPO as a hw exercise.
https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdXPOMDPs 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 - Deep Learning Study GroupLink visible for attendees
Deep learning is evolving quickly. Important new developments are appearing daily. This group attempts to keep up by reading and discussing current deep learning literature. This meetup uses discussion among the participants to speed understanding of current research results. That requires that some participants read the paper before attending. Anyone is welcome to attend and listen without reading the paper. If nobody reads the paper the meeting will be short.
Paper for June 24, 2025:
Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA
https://arxiv.org/pdf/2410.20672
OpenReview:
https://openreview.net/forum?id=WwpYSOkkCtPapers that we're reading, code that participants generate and other random stuff can be found at github site for the group.
https://github.com/davidmacmillan/DeepLearningStudyGroup - Reinforcement Learning Working GroupLink visible for attendees
RL Working Group
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=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdXPOMDPs 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
