MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.

LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units), and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC2. Other methods include the extended Kalman filter and some parameter optimization routines. LibBi consists of a C++ template library and a parser and compiler, written in Perl, for its own modelling language.

FroZenLight interrelates line arts, mathematics, and cryptography. Circular shaped mirrors which are arranged in a grid-like manner reflect a light ray according to the reflection law of geometric optics. While random positions of the light source produce chaotic reflection patterns, it is possible to position the light source so that beautiful symmetric reflection patterns are created.

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features tight integration with numpy, transparent use of a GPU, efficient symbolic differentiation, speed and stability optimizations, dynamic C code generation, and extensive unit-testing and self-verification. Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal).

Hydra Slayer is a Roguelike game focused on one thing: slaying Hydras. It is inspired by mathematical puzzles about brave heroes slaying many-headed beasts. Since each weapon can only cut off a specific number of heads (no more, no less), and then the Hydra regrows some of the lost heads, to defeat each Hydra, you need to find the sequence of attacks which kills it in the least number of wounds. Hydra Slayer also features divisor weapons, blunt weapons to stun heads, missiles, and shields, and a number of other magical items which are unique to this game.

DEDiscover is a workflow-based differential equation modeling software tool for scientists, statisticians, and modelers. Whether you need to do quick simulation, develop sophisticated models, or teach mathematical concepts, DEDiscover combines a powerful computation engine with a user-friendly interface to give you a tool that's better, faster, and easier-to-use.

TSPSG is intended to generate and solve "travelling salesman problem" (TSP) tasks. It uses the Branch and Bound method for solving. Its input is a number of cities and a matrix of city-to-city travel costs. The matrix can be populated with random values in a given range (which is useful for generating tasks). The result is an optimal route, its price, step-by-step matrices of solving, and a solving graph. The task can be saved in an internal binary format and opened later. The result can be printed or saved as PDF, HTML, or ODF. TSPSG may be useful for teachers to generate test tasks or just for regular users to solve TSPs. Also, it may be used as an example of using the Branch and Bound method to solve a particular task.