What we’re about
Silicon Valley Generative AI is a dynamic community of professionals, researchers, startup founders, and enthusiasts who share a passion for generative AI technology. As part of the wider GenAI Collective network, the group provides a fertile ground for the exploration of cutting-edge research, applications, and discussions on all things related to generative AI.
Our community thrives on two main types of engagement. Firstly, in partnership with Boulder Data Science, we host bi-weekly "Paper Reading" sessions. These meetings are designed for deep-dives into the latest machine learning papers, fostering a culture of continuous learning and collaborative research. It's an excellent opportunity for anyone looking to understand the nitty-gritty scientific advancements propelling the field forward.
Secondly, we organize monthly "Talks" that offer a broader range of insights into the world of generative AI. These sessions feature presentations by an eclectic mix of speakers, from industry pioneers and esteemed researchers to emergent startup founders and subject matter experts. Unlike the paper reading sessions, which are more academically inclined, the talks are tailored to appeal to a more general audience. Topics can span the gamut from the technical intricacies of the latest generative models to their real-world applications, startup pitches, and even discussions on the legal and ethical implications of AI.
Whether you're a seasoned professional or merely curious about generative AI, Silicon Valley Generative AI provides a comprehensive platform to learn, discuss, and network.
We strive to be an inclusive community that fosters innovation, knowledge-sharing, and a collective drive to shape the future of AI responsibly. Join us to stay at the forefront of generative AI research, news, and applications.
For those eager to dive deeper into the technical aspects, you can join us on the GenAI Collective Slack, specifically the #discuss-technical channel, to keep the conversations flowing between meetups.
We are also looking for the following:
• Readers: people who are willing to read papers and speak about them.
• Speakers and presenters: who will put together educational materials and present to the group as well as answer questions.
• Industry events: if you have a generative AI event like a hackathon, lunch and learn or an information session on your product, we would be happy to include in the calendar.
Please contact Matt White here or at contact@matt-white.com
Upcoming events (4+)
See all- Reinforcement Learning: Dynamic Programming Value Iteration and ExamplesLink visible for attendees
Last meeting we began Chapter 4 and covered how policy evaluation can use a form a fixed-point iteration to converge to the correct value function of a policy. We then proved the policy improvement theorem and made use of it in the policy iteration algorithm. If you didn't attend that meeting, you can find the recording of it in the YouTube playlist below since this session will build off what was covered there.
This meeting we will finish Chapter 4 by covering some specific examples of policy iteration and then introduce the value iteration algorithm with more examples.
As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Useful Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
Video lectures from a similar course - Reinforcement Learning: Topic TBALink visible for attendees
Typically covers chapter content from Sutton and Barto's RL book
As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Useful Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
Video lectures from a similar course
