Looking to learn more about Fullstack development or maybe Type-level and implicits is more to your interest? What about Reinforcement Learning?
So, don't worry if you didn't make it along on Thursday, you can watch each of the talks below.
Thank you again to our awesome speakers, the talks were very interesting so we highly recommend checking them out!
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An overview of Scala in the City at Intuit
Cristian Arcaroli - Modern Fullstack in Scala
This talk is a journey through Fullstack development using Scala with modern technologies like React, Apollo Client, and GraphQL.
Daniel Ciocîrlan - Type-Level Sorting in Scala
Chris Birchall - Reinforcement Learning in Scala
During this talk you will find out what RL is all about and how to implement it in Scala. Chris will introduce RL, providing use cases and intuition about what kind of problems it can solve. He'll also share some of its core concepts, including Markov Decision Processes, policies and action values, prediction and control, exploitation vs exploration and bootstrapping.
Next we'll implement some of these concepts in Scala, starting from scratch and working step by step towards an implementation of 'Q-learning' – a popular RL technique for learning policies. We'll structure our code using type classes to separate the generic Q-learning framework from the specifics of any particular problem we want to model.
You will also learn how to train an agent using your Q-learning implementation, and finally Chris will demonstrate the result of the training: the computer successfully playing a simple game.