KirillZubov/nlp_course
YSDA course in Natural Language Processing
YSDA course in Natural Language Processing
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Operator learning in Julia.
Local, global, and beyond optimization for scientific machine learning (SciML)
saved trained neural operator models
Stack trace visualizer
Write flame graphs to SVG format and explore them interactively in Jupyter, Pluto, etc.
Benchmarks for scientific machine learning (SciML) software and differential equation solvers
A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Relax! Flux is the ML library that doesn't make you tensor
MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.
A fast and modern CAS for a fast and modern language.
The Base interface of the SciML ecosystem
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)
Notebook for running Julia on Google Colab
Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF)
DiffEq solvers for stochastic differential equations
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
Deep BSDE solver in TensorFlow
Scala command line checklist checker