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Thank you to all who attended Scala in the City at Intuit

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We had another amazingly successful Scala in the City and it's thanks to all of you for your support!

It was awesome to get the chance to see inside the stunning Intuit offices and meet the engineering team, thank you to Intuit Engineer Manager and Lead Engineer Juan José Coello for telling us a bit more about the Intuit story.

Our speakers, Cristian ArcaroliDaniel Ciocîrlan and Chris Birchall were incredible and we were very lucky they gave up their evenings to give us their Scala insights, thanks guys.

If you missed out on last night's Scala in the City then don't worry as we will have the videos with you very soon.

In the meantime, check out the slides from each of the talks.

Stay posted for the announcement of April Scala in the City, it's one you won't want to miss!


Cristian Arcaroli - Modern Fullstack in Scala

As Scala developers we tend to prefer to work with strongly typed languages and in particular with Scala because we know it gives us more confidence and more tools. With the libraries/framework currently available it's possible to have this property throughout our whole codebase.
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

Scala's type system and implicit resolution mechanism allow for sophisticated type-level relationship inference. In this talk, we will see how we can impose type-level constraints and determine complex relationships between types, enough to be able to sort types... at compile time.

Chris Birchall - Reinforcement Learning in Scala

Reinforcement learning (RL) is a powerful machine learning paradigm that has been successfully applied to a wide class of problems, from steering helicopters to predicting stock prices.

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.
See you next month!