Data Science

It's time for another Kotlin Dataframe update to start off the new year.There have been a lot of exciting changes since the last 0.8.0 preview release. So without any further ado, let's jump right in! TL;DR: OpenAPI type schemas can now be parsed and converted into data schemas.New JSON reading options include type clash tactics and key/value paths.Support for writing Apache Arrow files has been added.Many bugs have been fixed.Make sure to update your Kotlin Jupyter kernel if you use DataFrame there. Kotlin DataFrame on GitHub OpenAPI Type Schemas JSON schema inference is great,

Version 0.5 of our deep learning library, KotlinDL, is now available! This release focuses on the new API for the flexible and easy-to-use deployment of ONNX models on Android. We have reworked the Preprocessing DSL, introduced support for ONNX runtime execution providers, and more. Here's a summary of what you can expect from this release: Android supportPreprocessing DSLInference on accelerated hardwareCustom models inference KotlinDL on GitHub Android demo app (more…)

Introducing Multik 0.2.0! Now a multiplatform library, it allows you to use multidimensional arrays in your favorite multiplatform projects. Let's take a closer look at what’s new in v0.2.0. Multik on GitHub (more…)

TL;DR: We at the Kotlin team have developed a Kotlin library for data frames. Today we’re releasing its first public preview version. It provides a readable and powerful DSL for data wrangling and i/o via CSV, JSON, Excel, and Apache Arrow, as well as interop with Kotlin data classes and hierarchical data schemas. The library is ready for you to try, and we’re keen to get your feedback. Kotlin DataFrame on GitHub Today we’re unveiling a new member of the collection of Kotlin libraries for data science. We’ve previously written about KotlinDL for deep learning and Multik for tensors.

Version 0.4 of our deep learning library, KotlinDL, is out! KotlinDL 0.4 is now available on Maven Central with a variety of new features – check out all of the changes that are coming to the new release! We’re currently introducing new models in ModelHub (including the EfficientNet and EfficientDet model families), the experimental high-level Kotlin API for Pose Detection, new layers and preprocessors contributed by the community members, and many other changes. KotlinDL on GitHub In this post, we’ll walk you through the changes to the Kotlin Deep Learning library in the 0.4 release:

Alexey Zinoviev presented the webinar “Object Detection and Image Recognition with Kotlin,” where he explored a deep learning library written in Kotlin, described how to detect objects of different types in images, and explained how to create a Kotlin Web Application using Ktor and KotlinDL that recognizes cars and persons on photos. He has decided there is more that he would like to share with you on the subject, and so here is an extended article.

Introducing Multik 0.1 - a new, enhanced version of our multidimensional array library! You can check out the previous post to learn about the basic features and architecture of the library. In the new release, we added new methods from linear algebra, supported complex numbers and reading/writing .csv files, improved the performance and stability of existing functions, and added many more features that will make it easier for you to work with multidimensional arrays. Multik on GitHub Let’s take a look at the new features this release brings to the API: Reading and writing CSV file

KotlinDL 0.3 is available now on Maven Central with a variety of new features! New models in ModelHub (including the first Object Detection and Face Alignment models), the ability to fine-tune the Image Recognition models saved in ONNX format from Keras and PyTorch, the experimental high-level Kotlin API for image recognition, a lot of new layers contributed by the community members and many other changes.

KotlinDL 0.2 is available now on Maven Central with a variety of new features. New layers, a special Kotlin-idiomatic DSL for image preprocessing, a few types of Datasets, a great Model Zoo with support for the ResNet and MobileNet model families, and many more changes are now receiving a final polish.

This update of the Kotlin kernel for Jupyter Notebook primarily targets library authors and enables them to easily integrate Kotlin libraries with Jupyter notebooks. It also includes an upgrade of the Kotlin compiler to version 1.5.0, as well as bug fixes and performance improvements. pip installConda install The old way to add library integrations As you may know, it was already possible to integrate a library by creating a JSON file, which we call a library descriptor. In the kernel repository, we have a number of predefined descriptors. You can find the full list of them here. Creating lib

We have released Preview 2 of Kotlin for Apache Spark. First of all, we’d like to thank the community for providing us with all their feedback and even some pull requests! Now let’s take a look at what we have implemented since Preview 1? Scala 2.11 and Spark 2.4.1+ support The main change in this preview is the introduction of Spark 2.4 and Scala 2.11 support. This means that you can now run jobs written in Kotlin in your production environment. The syntax remains the same as the Apache Spark 3.0 compatible version, but the installation procedure differs a bit. You can now use sbt to

You can understand a lot about data from metrics, checks, and basic statistics. However, as humans, we grasp trends and patterns way quicker when we see them with our own eyes. If there was ever a moment you wished you could easily and quickly visualize your data, and you were not sure how to do it in Kotlin, this post is for you! Today I’d like to talk to you about Lets-Plot for Kotlin, an open-source plotting library for statistical data written entirely in Kotlin. You’ll learn about its API, the kinds of plots you can build with it, and what makes this library unique. Let’s start with the