Gradient Descent

We sit down with Erik to discuss gradient descents and how understanding this can lead to demystifying machine learning. We talk about the value of knowing how things work under the covers, as well as maths, functional programming, and many other topics, including why he chose Kotlin, and what he likes and dislikes of the language

About Erik Meijer

Erik Meijer has been trying to bridge the ridge between theory and practice for most of his career. He is perhaps best known for his work on, amongst others, the Haskell, C#, Visual Basic, and Dart programming languages, as well as for his contributions to LINQ and the Reactive Framework (Rx). Most recently he is on a quest to make uncertainty a first-class citizen in mainstream programming languages

Show Notes

Additional notes

MVIKotlin and Decompose with Arkadii Ivanov from Bumble

In this episode, guest Arkadii Ivanov takes us on a journey through his open-source projects MVIKotlin and Decompose. Find out about the Model-View-Intent architecture pattern, learn how Decompose helps write better declarative multiplatform user interfaces with Kotlin, and discover that time travel is possible – at least for debugging. Continue reading

Kotlin Multiplatform with John O’Reilly

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