Stats/ML and also DSP in Julia.
JuMP support many types of optimisation, including over non-continuous domains, and is part of the JuliaOpt family of confusingly diverse optimizers, which invoke various sub-families of optimizers. The famous NLOpt solvers comprise one such class, and they can additionally be invoked separately.
Unlike NLOpt and the JuMP family, Optim.jl (part of JuliaNLSolvers, a different family entirely) solves optimisation problems purely inside Julia. It has nieces and nephews such as LsqFit for Levenberg-Marquardt non-linear least squares fits. Optim will automatically invoke ForwardDiff. Assumes mostly unconstrained problems.
Krylov.jl is a collection of Krylov-type iterative method for large iterative linear and least-squares objectives.
Julia is a hotbed of autodiff for technical and community reasons. Such a hotbed that it’s worth discussing in the autodiff notebook.
Closely related, projects like ModelingToolkit.jl blur the lines between equations and coding, and allow easy definition of differentiable or probabilistic programming.
Statistics, probability and data analysis
Hayden Klok and Yoni Nazarathy are writing a free Julia Statistics textbook which seems a thorough introduction to statistics as well as Julia, albeit statistics in a classical frame that won’t be fashionable with either your learning theory or Bayesian types.
A good starting point for doing stuff is JuliaStats which organisation produces many statistics megapackages, for kernel density estimates, generalised linear models, loess etc. Install them all using Statskit:
The workhorse data structure of statistics.
Data frames are provided by DataFrames.jl.
There are some older ones you might encounter such as DataTables.jl which are subtly incompatible in tedious ways which these days we can ignore. Legacy compatability is provided by IterableTables.jl to translate where needed between these and many more useful other data sources.
using RDatasets iris = dataset("datasets", "iris") neuro = dataset("boot", "neuro")
DataFrames taste better with InvertedIndices.
Lasso and other sparse regressions are available in Lasso.jl which reimplements the lasso algorithm in pure Julia, GLMNET.jl which wrap the classic Friedman FORTAN code for same. There is also (functionality unattested) an orthogonal matching pursuit one called OMP.jl but that algorithm is simple enough to bang out oneself in an afternoon, so no stress if it doesn’t work. Incremental/online versions of (presumably exponential family) statistics are in OnlineStats. MixedModels.jl
is a Julia package providing capabilities for fitting and examining linear and generalized linear mixed-effect models. It is similar in scope to the
lme4package for R.
Probabilistic programming! Bayesian inference considered broadly! Several option under the probabilistic programming page are based on julia, specifically, Turing.jl, Mamba.jl, Gen, DynamicHMC, Klara.jl, and probably others.
Let’s put the automatic differentiation, the optimizers and the samplers together to do differentiable learning!
The deep learning toolkits have shorter feature lists than the lengthy ones of those fancy python/C++ libraries (e.g. mobile app building, cuDNN-backed optimisations are all less present in julia libraries) But maybe elegance/performance of Julia makes some of those features irrelevant? I for one don’t care about most of those because I’m a researcher not a deployer.
Having said that, Tensorflow.jl gets all the features, because it invokes C++ tensorflow. Surely one misses the benefit of Julia this way, since there are two different array-processing infrastructures to data between, and a different approach to JIT versus pre-compiled execution. Or no?
Flux.jl sounds like a reimplementation of Tensorflow-style differentiable programming inside Julia, which strikes me as the right way to do this to benefit from the end-to-end-optimised design philosophy of Julia.
Flux is a library for machine learning. It comes “batteries-included” with many useful tools built in, but also lets you use the full power of the Julia language where you need it. The whole stack is implemented in clean Julia code (right down to the GPU kernels) and any part can be tweaked to your liking.
It’s missing some features of Tensorflow, bu includes compensatory suprising/unique feature combinations. GPU support supposes that CuArrays can represent all the operations I need and will perform them optimally, and that I don’t need any fancy DNN-specific GPU optimizations. I suspect this requires careful footwork to function. This is dubious – For example CuArrays do not support all the FFT operations I want, such as the Discrete Cosine Transform. However, maybe it is usually enough.
Alternatively, Mocha.jl is a belt-and-braces deep learning thing, with a library of pre-defined layers. Swap out some of the buzzwords of Flux with newer ones, and skip some older ones, and there you are. It doesn’t appear, on brief perusal, to be as flexible as Flux, missing state filters and recurrent nets and many other models that are made less pure by their distance from the platonic ideal of deep learning, the convnet. As such it is not much use to me, despite promising clever things about using CUDA etc. YMMV.
Knet.jl is another deep learning library that claims to show the ease of implementing deep learning frameworks in Julia. Their RNN support looks a little limp, and they are using the less-popular autodiff frameworks, so I won’t be using ’em but point is well taken.
If one were aiming to do that, why not do something left-field like use the dynamical systems approach to deep learning? This neat trick was popularised by Haber and Ruthotto et al, who have released some of their models as Meganet.jl. I’m curious to see how they work.
A straight, if not fancy package for
Gaussian Process is
It aspires to play well with
Turing.jl for non-Gaussian likelihood
Flux.jl for deep Gaussian processes.
Currently has automatic differentiation problems.
ODEs, PDEs, SDEs
Chris Rauckackas is a veritable wizard with this stuff; just read his blog.
Here is a tour of fun tricks with stochastic PDEs. There is a lot of tooling for this; DiffEqOperators … does something. DiffEqFlux (EZ neural ODEs works with Flux and claims to make Neural ODEs simple. The implementation of these things in python, for the award-winning NeurIPS paper that made them famous was a nightmare. +1 for Julia here. The neural SDE section is mostly julia; Go check that out.
FFTs are provided by AbstractFFTs, which in-principle wraps many FFT implementations. I don’t know if there is a GPU implementation yet, but there for sure is the classic CPU implementation provided by FFTW.jl which uses FFTW internally.
JuliaAudio processes audio. They recommend PortAudio.jl as a real time soundcard interface, which looks sorta simple. See rkat’s example of how this works. There are useful abstractions like SampledSignals to load audio and keep the data and signal rate bundled together. Although, as SampledSignal maintainer Spencer Russell points out, AxisArrays might be the right data structure for sample signals, and you could use SampledSignals purely for IO, and ignore its data structures thereafter.
Images.jl processes images.
Low discrepancy and other QMC stuff. Mostly I want low discrepancy sequences. There are two options with near identical interfaces; I’m not sure of the differences.
Sobol.jl claims to have been performance profiled:
] add Sobol using Sobol s = SobolSeq(2) # Then x = next!(s)
] add https://github.com/PieterjanRobbe/QMC.jl using QMC lat = LatSeq(2) #then next(lat)