Taliesin Beynon
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library
Projects
2020
MCTS in Swift
AlphaZero and MCTS implementation for OpenSpiel
This project is a work-in-progress fork of the OpenSpiel library. It adds an MCTS implementation that will form the basis of a simple AlphaZero trainer. The design leverages some of the powerful type system abilities of Swift to achieve a maximally composable design, which allows the underlying tree-building implementation to be shared between several algorithms (various variants of MCTS, as well as Alpha-Beta search), which additionally makes them very clear. The fundamental Swift constructs …
2016
Deep Learning for Mathematica
high-level, flexible, friendly deep learning
From 2016 to 2018, I led development of the neural network framework, which encompassed 50+ functions and objects. Since version 10.3 it has been part of the core Mathematica language. It covers a wide range of state-of-the-art deep learning functionality, in an interface that is extremely easy for scientists and engineers to use, rivaling and exceeding the ease-of-use of libraries like Keras. One notable use of the framework was to replace the filter-bank based gravitional wave detectors used …
2015
Cryptography for Mathematica
user-friendly cryptography API
Semantic Import for Mathematica
automated import and recognition of rich tabular data
Text Search for Mathematica
easy-to-use lucene-backed indexing engine
2014
Hierarchical Data in Mathematica
DataFrame-like construct for data science workflows
For Mathematica 11, I contributed the the new Dataset construct in Mathematica, which acts as a flexible container for tabular data. It involved a lot of interesting challenges around automated layout and deduction of types from unstructured data. The underlying technology scales to quite complicated nested use of Mathematica’s two native data structures, being lists $\{v_1,v_2,...\}$ and associations $\left<\left| k_1 \to v_1,k_2 \to v_2,... \right|\right>$. Because Mathematica is a …