Pinned Repositories
ContinuousNormalizingFlows.jl
Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
Sinograms.jl
Julia library for working with sinograms / tomography / Radon transform
ImageReconstruction.jl
julia
The Julia Programming Language
EC-Class-HW1-GA
EC-Class-HW1-GA
mnist-nn
MNIST-Recognition
MNIST-Recognition
NN-Class-HW1-MLP
NN-Class-HW1-MLP
PDFEstimators.jl
DiffEqFlux.jl
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
prbzrg's Repositories
prbzrg/PDFEstimators.jl
prbzrg/EC-Class-HW1-GA
EC-Class-HW1-GA
prbzrg/mnist-nn
prbzrg/MNIST-Recognition
MNIST-Recognition
prbzrg/NN-Class-HW1-MLP
NN-Class-HW1-MLP
prbzrg/NN-Class-HW2-HopfieldNetwork
NN-Class-HW2-HopfieldNetwork
prbzrg/ThesisExperiments
ThesisExperiments
prbzrg/AbstractDifferentiation.jl
An abstract interface for automatic differentiation.
prbzrg/DiffEqFlux.jl
Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
prbzrg/Sinograms.jl
Julia library for working with sinograms / tomography / Radon transform
prbzrg/ADTypes.jl
Repository for SciML AD backend types
prbzrg/AllocCheck.jl
AllocCheck
prbzrg/awesome-normalizing-flows
Awesome resources on normalizing flows.
prbzrg/DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
prbzrg/Distributions.jl
A Julia package for probability distributions and associated functions.
prbzrg/DistributionsAD.jl
Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff
prbzrg/dotgithub-sciml
Organization-wide .github actions and other metadata
prbzrg/Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
prbzrg/julia
The Julia Programming Language
prbzrg/Lux.jl
Explicitly Parameterized Neural Networks in Julia
prbzrg/MKL.jl
Intel MKL linear algebra backend for Julia
prbzrg/Optimization.jl
Local, global, and beyond optimization for scientific machine learning (SciML)
prbzrg/OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
prbzrg/PrecompileTools.jl
Reduce time-to-first-execution of Julia code
prbzrg/ReverseDiff.jl
Reverse Mode Automatic Differentiation for Julia
prbzrg/SciMLBase.jl
The Base interface of the SciML ecosystem
prbzrg/SciMLSensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
prbzrg/SimpleNonlinearSolve.jl
Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
prbzrg/WeightInitializers.jl
Weight Initialization Schemes for Deep Learning Frameworks
prbzrg/Zygote.jl
21st century AD