Search Results for "can=" - Page 4
Sort By:
Showing 656 open source projects for "can="
View related business solutions-
$300 in Free Credit Towards Top Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
-
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
-
1
Scanopy
Clean network diagrams, One-time setup, zero upkeep
...It is built to handle complex scanning tasks — such as OCR, document analysis, audio transcription, network data capture, and image extraction — while providing unified APIs and workflows that make managing heterogeneous data sources seamless. Developers can compose custom pipelines that chain together transforms, filters, and exporters, enabling automation of tedious data preparation steps and accelerating insights with minimal code. The system places a premium on extensibility, allowing contributors to add new extractors or analysis modules tailored to specific industries or datasets. ... -
2
SimpleTraits.jl
Simple Traits for Julia
This package provides a macro-based implementation of traits, using Tim Holy's trait trick. The main idea behind traits is to group types outside the type-hierarchy and to make dispatch work with that grouping. The difference to Union-types is that types can be added to a trait after the creation of the trait, whereas Union types are fixed after creation. The cool thing about Tim's trick is that there is no performance impact compared to using ordinary dispatch. For a bit of background and a quick introduction to traits watch my 10min JuliaCon 2015 talk. -
3
Tullio.jl
Tullio is a very flexible einsum macro
...It understands many array operations written in index notation -- not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting. Used by itself the macro writes ordinary nested loops much like Einsum.@einsum. One difference is that it can parse more expressions, and infer ranges for their indices. Another is that it will use multi-threading (via Threads.@spawn) and recursive tiling, on large enough arrays. -
4
esquisse
RStudio add-in to make plots interactively with ggplot2
The purpose of this add-in is to let you explore your data quickly to extract the information they hold. You can create visualization with {ggplot2}, filter data with {dplyr} and retrieve generated code. This addin allows you to interactively explore your data by visualizing it with the ggplot2 package. It allows you to draw bar plots, curves, scatter plots, histograms, boxplot and sf objects, then export the graph or retrieve the code to reproduce the graph. -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
-
5
see
Visualisation toolbox for beautiful and publication-ready figures
see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It... -
6
PyVista
3D plotting and mesh analysis through a streamlined interface
...This package provides a Pythonic, well-documented interface exposing VTK’s powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets. This module can be used for scientific plotting for presentations and research papers as well as a supporting module for other mesh-dependent Python modules. Easily integrate with NumPy and create a variety of geometries and plot them. You could use any geometry to create your glyphs, or even plot the points directly. Direct access to mesh analysis and transformation routines. ... -
7
ipychart
The power of Chart.js with Python
...The charts created are fully configurable, interactive, and modular and are displayed directly in the output of the cells of your jupyter notebook environment. Charts are fully interactive, you can hover it to display tooltips and select the information you want to see directly from the output cell of your notebook. All the types of charts present in Chart.js are exposed in ipychart. Even complex features such as mixed-types charts are available. Charts are highly customizable and all Chart.js options are available in ipychart. ... -
8
Kale
Kubeflow’s superfood for Data Scientists
KALE (Kubeflow Automated pipeLines Engine) is a project that aims at simplifying the Data Science experience of deploying Kubeflow Pipelines workflows. Kubeflow is a great platform for orchestrating complex workflows on top Kubernetes and Kubeflow Pipeline provides the mean to create reusable components that can be executed as part of workflows. The self-service nature of Kubeflow make it extremely appealing for Data Science use, at it provides an easy access to advanced distributed jobs orchestration, re-usability of components, Jupyter Notebooks, rich UIs and more. Still, developing and maintaining Kubeflow workflows can be hard for data scientists, who may not be experts in working orchestration platforms and related SDKs. ... -
9
JuliaConnectoR
A functionally oriented interface for calling Julia from R
...The results of function calls are likewise translated back to R. Complex Julia structures can either be used by reference via proxy objects in R or fully translated to R data structures. -
Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
-
10
CondaPkg.jl
Add Conda dependencies to your Julia project
...CondaPkg will install these dependencies into a Conda environment specific to the current Julia project. Hence dependencies are isolated from other projects or environments. Functions like add, rm, status exist to edit the dependencies programmatically. Or you can do pkg> conda add some_package to edit the dependencies from the Pkg REPL. -
11
NBInclude.jl
import code from IJulia Jupyter notebooks into Julia programs
NBInclude is a package for the Julia language that allows you to include and execute IJulia (Julia-language Jupyter) notebook files just as you would include an ordinary Julia file. The goal of this package is to make notebook files just as easy to incorporate into Julia programs as ordinary Julia (.jl) files, giving you the advantages of a notebook (integrated code, formatted text, equations, graphics, and other results) while retaining the modularity and re-usability of .jl files. -
12
JuliaWorkshop
Intensive Julia workshop that takes you from zero to hero
This is an intensive workshop for the Julia language, composed out of three 2-hour segments. It targets people already familiar with programming, so that the established basics such as for-loops are skipped through quickly and efficiently. Nevertheless, it assumes only rudimentary programming familiarity and does explain concepts that go beyond the basics. The goal of the workshop is to take you from zero to hero (regarding Julia): even if you know nothing about Julia, by the end you should... -
13
ChainRulesCore
AD-backend agnostic system defining custom forward and reverse rules
...The ChainRulesCore package provides a light-weight dependency for defining sensitivities for functions in your packages, without you needing to depend on ChainRules itself. This will allow your package to be used with ChainRules.jl, which aims to provide a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives. -
14
DynamicHMC
Implementation of robust dynamic Hamiltonian Monte Carlo methods
...In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent of coding a (log) posterior density in Julia. This approach allows the use of standard tools like profiling and benchmarking to optimize its performance. -
15
ChainRules.jl
Forward and reverse mode automatic differentiation primitives
The ChainRules package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse--, and mixed-mode primitives. The core logic of ChainRules is implemented in ChainRulesCore.jl. To add ChainRules support to your package, by defining new rules or frules, you only need to depend on the very light-weight package ChainRulesCore.jl. -
16
luma.gl
High-performance Toolkit for WebGL-based data visualization
luma.gl is a GPU toolkit for the Web-focused primarily on data visualization use cases. luma.gl aims to provide support for GPU programmers that need to work directly with shaders and want a low abstraction API that remains conceptually close to the WebGPU and WebGL APIs. Unlike other common WebGL APIs, the developer can choose to use the parts of luma.gl that support their use case and leave the others behind. While generic enough to be used for general 3D rendering, luma.gl's mandate is primarily to support GPU needs of data visualization frameworks in the vis.gl suite. -
17
AMDGPU.jl
AMD GPU (ROCm) programming in Julia
AMD GPU (ROCm) programming in Julia. -
18
Ferrite.jl
Finite element toolbox for Julia
A simple finite element toolbox written in Julia. -
19
Polyhedra
Polyhedral Computation Interface
Polyhedra provides an unified interface for Polyhedral Computation Libraries such as CDDLib.jl. This manipulation notably includes the transformation from (resp. to) an inequality representation of a polyhedron to (resp. from) its generator representation (convex hull of points + conic hull of rays) and projection/elimination of a variable with e.g. Fourier-Motzkin. -
20
mapcn
Beautiful map components, 100% Free, Zero config, one command setup
mapcn is a research-oriented project centered on mapping continuous control in reinforcement learning to structured policies using neural networks. It explores how high-dimensional action spaces can be decomposed into structured primitives that can be learned, composed, and reused across different tasks. The core idea is to enable agents to generalize learned behavior by representing continuous control policies in a compact, interpretable form that preserves smoothness and controllability. The project includes implementations that experiment with policy encoding, action decomposition techniques, and sample efficiency analysis in classic reinforcement learning environments. ... -
21
errsole.js
Collect, Store, and Visualize Logs with a Single Module
Errsole is an open-source logger for Node.js. It has a built-in web dashboard to view, filter, and search your app logs. -
22
gt R
Easily generate information-rich, publication-quality tables from R
With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). -
23
BetaML.jl
Beta Machine Learning Toolkit
...All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its implementation. Thanks to its JIT compiler, Julia is indeed in the sweet spot where we can easily write models in a high-level language and still have them running efficiently. -
24
InteractiveViz.jl
Interactive visualization tools for Julia
...To allow generation of data points on demand through a graphics pipeline, requiring computation only at a level of detail appropriate for display at the viewing resolution. Additional data points can be generated on demand when zooming or panning. This package was partly inspired by the excellent Datashader package available in the Python ecosystem. -
25
ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
...ImplicitGlobalGrid relies on the Julia MPI wrapper (MPI.jl) to perform halo updates close to hardware limit and leverages CUDA-aware or ROCm-aware MPI for GPU-applications. The communication can straightforwardly be hidden behind computation [1, 3] (how this can be done automatically when using ParallelStencil.jl is shown in; a general approach particularly suited for CUDA C applications is explained in.
