mossr/BeautifulAlgorithms.jl is ranked 10,358 among these 8 peers; BilalReffas/EmojiIntelligence is ranked 10,354.

jl Concise algorithms written in Julia and formatted with Carbon. Algorithms for machine learning, optimization, reinforcement learning, online planning, decision making under uncertainty, and sorting. All implementations are working and self-contained; refer to the test cases. _Note, these are primarily for academic purposes and are not designed for real-world usage. There are many other Julia packages that implement more sound versions of these algorithms. _ Gradient descent Stochastic gradient descent Two-layer neural network Multi-layer neural network Loss

functions Distance functions Nearest neighbor K-nearest neighbors K-means clustering The EM algorithm Linear regression Ridge regression Basis regression Radial basis regression Logistic regression Cross-entropy method Finite difference methods Simulated annealing Twiddle Newton's method Gaussian process Thompson sampling Particle filter Value iteration Branch and bound Monte Carlo tree search Huffman coding Hailstone sequence (Collatz conjecture) Bubble sort Merge sort Insertion sort Bogo sort Quine *Note: Algorithms are modified from their original sources. * Gradient descent Percy Liang and Dorsa

Sadigh, *Artificial Intelligence: Principles and Techniques*, Stanford University, 2019. Stochastic gradient descent Percy Liang and Dorsa Sadigh, *Artificial Intelligence: Principles and Techniques*, Stanford University, 2019. Two-layer neural network Two-layer neural network (one-liner) Multi-layer neural network Loss functions Distance functions Nearest neighbor K-nearest neighbors K-means clustering Percy Liang and Dorsa Sadigh, *Artificial Intelligence: Principles and Techniques*, Stanford University, 2019. The EM algorithm Andrew Ng, *Mixtures of Gaussians and the EM algorithm*, Stanford University, 2020. Wheeler

, *Algorithms for Optimization*, MIT Press, 2019. (Credit @HenriDeh for use of ) Linear regression (one-liner) Ridge regression Basis regression Mykel J. Wheeler, *Algorithms for Optimization*, MIT Press, 2019. Wheeler, *Algorithms for Optimization*, MIT Press, 2019. Logistic regression Cross-entropy method Mykel J. Wheeler, *Algorithms for Optimization*, MIT Press, 2019. Wheeler, *Algorithms for Optimization*, MIT Press, 2019. Wheeler, *Algorithms for Optimization*, MIT Press, 2019. Twiddle Sebatian Thrun, *Artificial Intelligence for Robotics*, Udacity, 2012. Newton's method John Wallis, *A Treatise of Algebra

both Historical and Practical*, 1685. Wheeler, *Algorithms for Optimization*, MIT Press, 2019. Gaussian process kernels Thompson sampling Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen, *A Tutorial on Thompson Sampling*, arXiv:1707. Wray, *Algorithms for Decision Making*, Preprint. Wray, *Algorithms for Decision Making*, Preprint. Wray, *Algorithms for Decision Making*, Preprint. Wray, *Algorithms for Decision Making*, Preprint. Huffman, *A Method for the Construction of Minimum-Redundancy Codes*, IEEE, 1952. Hailstone sequence (Collatz conjecture) Bubble sort Karey

Shi, *Design and Analysis of Algorithms*, Stanford University, 2020. Merge sort Karey Shi, *Design and Analysis of Algorithms*, Stanford University, 2020. Insertion sort Karey Shi, *Design and Analysis of Algorithms*, Stanford University, 2020. Bogo sort Bogo sort (one-liner) Quine Nathan Daly, *Julia Discord*, 2019.