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Top Pkgs Packages

1

Flux

Relax! Flux is the ML library that doesn't make you tensor

2

IJulia

Julia kernel for Jupyter

3

DifferentialEquations

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

4

Gadfly

Crafty statistical graphics for Julia.

5

JuMP

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

6

Plots

Powerful convenience for Julia visualizations and data analysis

7

Turing

Bayesian inference with probabilistic programming.

8

Knet

Koç University deep learning framework.

9

Mocha

Deep Learning framework for Julia

10

DataFrames

In-memory tabular data in Julia

11

PyCall

Package to call Python functions from the Julia language

12

Zygote

Intimate Affection Auditor

13

Makie

High level plotting on the GPU.

14

PackageCompiler

Compile your Julia Package

15

TensorFlow

A Julia wrapper for TensorFlow

16

Revise

Automatically update function definitions in a running Julia session

17

UnicodePlots

Unicode-based scientific plotting for working in the terminal

18

ModelingToolkit

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

19

Optim

Optimization functions for Julia

20

Distributions

A Julia package for probability distributions and associated functions.

21

JuliaDB

Parallel analytical database in pure Julia

22

LightGraphs

An optimized graphs package for the Julia programming language

23

DSGE

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

24

Cxx

The Julia C++ Interface

25

Weave

Scientific reports/literate programming for Julia

26

Yao

Extensible, Efficient Quantum Algorithm Design for Humans.

27

OnlineStats

⚡ Single-pass algorithms for statistics ⚡

28

DiffEqFlux

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

29

ForwardDiff

Forward Mode Automatic Differentiation for Julia

30

OhMyREPL

Syntax highlighting and other enhancements for the Julia REPL