Julia implementation of the Euler's explicit and implicit methods for solving first order differential equations.
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Updated
Mar 20, 2014 - Julia
Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
Julia implementation of the Euler's explicit and implicit methods for solving first order differential equations.
Supplementary code and Jupyter notebooks for publication: Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle
Calculate Friedman formulas for large random numbers; attempt to prove that a large random number is a Friedman number
A collection of numerical algorithms for Julia.
Simple, yet playful, artificial neural network implementation for Julia language
UNMAINTAINED! Use https://github.com/bicycle1885/CodecZstd.jl instead
Julia doodles for generating images compliant with the rules of allRGB.com
A futuristic crypto library. In Julia. [OLD]
Logistic Regression from Crowds
Textbook implementation of backprop (from the Jacobian point of view) in Julia.
Converts a chess game written down in PGN format to a list of FEN positions
ArtBooster.jl turns images into abstract figures by predicting their features with a gradient booster in real-time.
Created by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman
Released February 14, 2012