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Videos from Scala in the City at Intuit #13

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Looking to learn more about Fullstack development or maybe Type-level and implicits is more to your interest? What about Reinforcement Learning?

Whatever one of those grabs your attention, you can learn about them all as we now have the Scala in the City videos from last week at Intuit ready for you to check out.

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

 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.


We want to hear from you!

Be part of the Scala in the City journey, whether you would love to host our meet-up at your offices or speak about your Scala projects we would be thrilled to have you involved. There are so many advantages to being a speaker, you have the chance to network, gain feedback on your projects and build your speaker CV.

If you think this might be of interest to you, get in touch for a chat on