Friday Hacks #192, September 4: Everything about Randomness, and Reinforcement Learning
Posted on by Chaitanya Baranwal
Date/Time: Friday, September 4 at 7:00pm
Venue: Online on Zoom
Zoom Link: https://www.nushackers.org/fh192zoom
Everything about Randomness
In this talk, we are going to discuss about all sorts of random number generation, such as: what does it mean to be random? What is a pseudo-random number generator? What is the difference between
/dev/urandom, and which one should we use? What makes a RNG secure, and why do we need to use SecureRandom for cryptographic purposes?
Herbert is a recent NUS graduate who currently works at Traverse Technologies (https://traverse.ai). He was a NUS Hackers coreteam member.
Reinforcement Learning with Ray RLlib
Reinforcement learning trains an agent to maximize a reward in an environment. I’ll start with why RL is important, how it works, several applications of RL, and also the compute challenges RL creates.
Then we’ll see how RLlib, implemented with Ray, seamlessly and efficiently supports RL, providing an ideal platform for building Python-based, RL applications with an intuitive, flexible API.
Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence (ML/AI). He is a Principal Software Engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable ML with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Dean is the author of several books and reports from O’Reilly, including the forthcoming report “What Is Ray? Distributed Computing Made Simple”. He is a contributor to several open source projects and a frequent conference speaker. He also co-organizes several conferences around the world and several user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington. Find Dean on Twitter: @deanwampler.
See you there!