Search Results for "rich"

Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose...

Julia already has a rich set of plotting tools in the form of the Plots and Makie ecosystems, and various backends for these. So why another plotting package? InteractiveViz is not a replacement for Plots or Makie, but rather a graphics pipeline system developed on top of Makie. It has a few objectives. To provide a simple API to visualize large or possibly infinite datasets (tens of millions of data points) easily.

A collection of tools for doing reinforcement learning research in Julia. Provide elaborately designed components and interfaces to help users implement new algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and...

...Furthermore, transducers can be defined without specifying the details of the input and output (collections, streams, channels, etc.) and therefore achieves a full reusability. Transducers are introduced by Rich Hickey, the creator of the Clojure language. His Strange Loop talk is a great introduction to the idea of transducers. Transducers.jl is an implementation of the transducers in Julia. Aiming to satisfy the high-performance needs of Julia users, Transducers.jl uses a formulation that is pure and aiding type-stability.

Add prefix ThreadsX. to functions from Base to get some speedup, if supported. The reduce-based functions support any collections that implement SplittablesBase.jl interface including arrays, Dict, Set, and iterator transformations. In particular, these functions support iterator comprehension. ThreadsX.jl is aiming at providing API compatible with Base functions to easily parallelize Julia programs. All functions that exist directly under ThreadsX namespace are public API and they implement...