Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Gophernotes | 3,679 | 1 | a month ago | 22 | May 31, 2022 | 52 | mit | Go | ||
The Go kernel for Jupyter notebooks and nteract. | ||||||||||
Tensorflow Internals | 2,664 | 2 years ago | 18 | TeX | ||||||
It is open source ebook about TensorFlow kernel and implementation mechanism. | ||||||||||
Hsuantienlin_machinelearning | 2,263 | 4 years ago | 3 | |||||||
Lgo | 2,082 | 3 years ago | 23 | bsd-3-clause | Go | |||||
Interactive Go programming with Jupyter | ||||||||||
Gpflow | 1,774 | 17 | 19 | 2 days ago | 39 | August 09, 2023 | 148 | apache-2.0 | Python | |
Gaussian processes in TensorFlow | ||||||||||
Vscode Jupyter | 1,143 | 2 | a day ago | 2 | October 03, 2022 | 617 | mit | TypeScript | ||
VS Code Jupyter extension | ||||||||||
Graph2vec | 791 | a year ago | 1 | gpl-3.0 | Python | |||||
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017). | ||||||||||
Tick | 441 | 1 | 1 | a month ago | 23 | May 24, 2020 | 77 | bsd-3-clause | Python | |
Module for statistical learning, with a particular emphasis on time-dependent modelling | ||||||||||
Erlemar.github.io | 321 | 2 years ago | Jupyter Notebook | |||||||
Data science portfolio | ||||||||||
Sars_tutorial | 308 | 3 years ago | 4 | mit | Python | |||||
Repository for the tutorial on Sequence-Aware Recommender Systems held at TheWebConf 2019 and ACM RecSys 2018 |
KernelFunctions.jl is a general purpose kernel package. It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations. The main goals of this package are:
x = range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ = SqExponentialKernel()
K₁ = kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ = Matern32Kernel() ∘ FunctionTransform(sin)
K₂ = kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ = PolynomialKernel(; c=2.0, degree=2) ∘ LinearTransform(randn(4, 1))
K₃ = kernelmatrix(k₃, x)
# Add and sum kernels
k₄ = 0.5 * SqExponentialKernel() * LinearKernel(; c=0.5) + 0.4 * k₂
K₄ = kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁" "K₂" "K₃" "K₄"],
)
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).