Search Results for "learn"
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Showing 28 open source projects for "learn"
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HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes, text or (mixed) lists. ... -
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Bayesian Optimization
Python implementation of global optimization with gaussian processes
...This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic tour notebook to learn how to use the package's most important features. Take a look at the advanced tour notebook to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more. Explore the options exemplifying the balance between exploration and exploitation and how to control it. Explore the domain reduction notebook to learn more about how search can be sped up by dynamically changing parameters' bounds. -
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Plots
Powerful convenience for Julia visualizations and data analysis
...If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. No need to change your code. No need to learn a new syntax. Plots might be the last plotting package you ever learn. -
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DFTK.jl
Density-functional toolkit
The density-functional toolkit, DFTK for short, is a collection of Julia routines for experimentation with plane-wave density-functional theory (DFT). The unique feature of this code is its emphasis on simplicity and flexibility with the goal of facilitating algorithmic and numerical developments as well as interdisciplinary collaboration in solid-state research. -
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plotly
An interactive graphing library for R
...There are two main ways to creating a plotly object: either by transforming a ggplot2 object (via ggplotly()) into a plotly object or by directly initializing a plotly object with plot_ly()/plot_geo()/plot_mapbox(). Both approaches have somewhat complementary strengths and weaknesses, so it can pay off to learn both approaches. Moreover, both approaches are an implementation of the Grammar of Graphics and both are powered by the JavaScript graphing library plotly.js, so many of the same concepts and tools that you learn for one interface can be reused in the other. Any graph made with the plotly R package is powered by the JavaScript library plotly.js. ... -
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Matplotlib
matplotlib: plotting with Python
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Matplotlib ships with several add-on toolkits, including 3D plotting with mplot3d, axes helpers in axes_grid1 and axis helpers in axisartist. A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot, ...), and a... -
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Metabase
The simplest, fastest way to share business intelligence and analytics
Metabase is the easiest way to let everyone in your company access business data and analytics, learn from it and ask questions. Even if you or your colleagues have no experience in SQL, you can easily summarize and visualize your data, share it and let your team ask questions about it. Metabase creates beautiful graphs and charts, with an easy-to-use dashboard where everyone can create, organize and share exceptionally visualized data. -
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DynamicalSystems.jl
Award winning software library for nonlinear dynamics timeseries
DynamicalSystems.jl is an award-winning Julia software library for nonlinear dynamics and nonlinear time series analysis. To install DynamicalSystems.jl, run import Pkg; Pkg.add("DynamicalSystems"). To learn how to use it and see its contents visit the documentation, which you can either find online or build locally by running the docs/make.jl file. DynamicalSystems.jl is part of JuliaDynamics, an organization dedicated to creating high-quality scientific software. All implemented algorithms provide a high-level scientific description of their functionality in their documentation string as well as references to scientific papers. ... -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
...It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function spaces. The kernel can be trained on different geometry, which is learned from a graph. Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation. ... -
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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units). ... -
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Observable Plot
A concise API for exploratory data visualization
Observable Plot is a free, open-source JavaScript library to help you quickly visualize tabular data. It has a concise and (hopefully) memorable API to foster fluency — and plenty of examples to learn from and copy-paste. In the spirit of show don’t tell, below is a scatterplot of the height and weight of Olympic athletes (sourced from Matt Riggott), constructed using a dot mark. We assign columns of data (such as weight) to visual properties (such as the dot’s x), and Plot infers the rest. You can configure much more, if needed, but Plot’s goal is to help you get a meaningful visualization quickly. ... -
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OrientDB
DBMS supporting graph, document, full-text and geospatial models
...OrientDB adheres to the NoSQL movement even though it supports ACID Transactions and SQL as query language. In this way it's easy to start using it without having to learn too much new stuff. -
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Chromatone
Chromatone is a digital garden of visual music theory
Cards and short overviews on the physics and physiology of vision and hearing and their intersection at visual music research, exploration, practice, and self-expression. Useful tools to have in the pocket like a pack of interactive cards to learn and use in everyday music practice. These are open source web experiments with different aspects of sound and color. Chromatone is an open source research and design project to explore, develop and implement the scientific way of visual music education, communication and performance. We've discovered complete and coherent Visual Music Language there. ... -
14
flot
Attractive JavaScript charts for jQuery
...Take a look at the the examples in examples/index.html; they should give a good impression of what flot can do, and the source code of the examples is probably the fastest way to learn how to use flot. Just include the JavaScript file after you've included jQuery. You need at least jQuery 1.2.6, but try at least 1.3.2 for interactive charts because of performance improvements in event handling. The plot function immediately draws the chart and then returns a plot object with a couple of methods. The placeholder is a jQuery object or DOM element or jQuery expression that the plot will be put into. -
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ScikitLearn.jl
Julia implementation of the scikit-learn API
The scikit-learn Python library has proven very popular with machine learning researchers and data scientists in the last five years. It provides a uniform interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters. ScikitLearn.jl brings these capabilities to Julia. Its primary goal is to integrate both Julia- and Python-defined models together into the scikit-learn framework. -
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Javis
Julia Animations and Visualizations
Javis: Julia Animations and Visualizations. Javis makes generating simple animations a breeze! Want to learn more? Check out our documentation for tutorials, our contributing guidelines, and the mission of Javis.jl. We have a live Zulip stream that you can join to discuss Javis with other Javis users. -
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Hedgehog Lab
Run, compile and execute JavaScript for Scientific Computing
Hedgehog Lab is an open-source scientific computation tool in the browser. Before the development, Pleases make sure you are already installed and enabled the yarn. Once cloned, switch to the dev branch and navigate to the folder by typing cd hedgehog-lab and then running the provided commands. On each run the program compiles and it takes time. Please wait until it shows "Compiled successfully!" and instructions about with which IP:PORT to connect. -
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Learn Julia the Hard Way
Learn Julia the hard way
The Julia base package is pretty big, although at the same time, there are lots of other packages around to expand it with. The result is that on the whole, it is impossible to give a thorough overview of all that Julia can do in just a few brief exercises. Therefore, I had to adopt a little 'bias', or 'slant' if you please, in deciding what to focus on and what to ignore. Julia is a technical computing language, although it does have the capabilities of any general-purpose language and... -
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StellarGraph
Machine Learning on Graphs
...StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn. -
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Facets
Visualizations for machine learning datasets
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. -
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AnyWave is a multi-platform software designed for neurologists and researchers who want to visualise and apply signal processing on electrophysiological signals. AnyWave can handle EEG, SEEG, MEG and more. MATLAB/Python/C++ plugins can be added. See this wiki section to learn how to build AnyWave on Linux : http://meg.univ-amu.fr/wiki/AnyWave:BuildSDK
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React Trend
Simple, elegant spark lines
Unfortunately, none of this project's authors have the time/enthusiasm to maintain this project right now. We recommend forking this project, or reading its source to learn how it's built). Graphing tools are often complex, abstract, and heavy. They require domain-specific knowledge and significant time investment. While building rich data visualizations for an analytics app, is a necessary cost. But what if you just need a sparkline that shows a trend? These are very simple and elegant visualizations, and they should not require a bloated graphing library to produce. ... -
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netmate
displays network protocol headers in uniform RFC like format
Netmate is a GTK+ program that shows network protocols headers in 32-bit aligned fields, identical as they are represented in Request for Comments (RFCs). It can be used to learn and teach about network protocols and to understand their functionality and collaboration. -
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AsphaltOpen is a project to visualize data collected during road asphalting operations. Asphalt contractors can use the software to visualize their operations, learn from the visualizations, and improve their processes continually.
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Dex
Data Visualization application written in JavaFX, Groovy, and HTML5.
Data Explorer, or Dex is a tool for exploring data. Dex is a JavaFX application which aims to provide a general purpose framework for data visualization. The basics are easy to learn, however Dex offers advanced capabilities in the form of SQL and groovy transformations. Dex integrates many excellent visualization frameworks into one consistent GUI package. This is an early release, so you are likely to find functional gaps for your particular problem space. In such cases, you are free to extend Dex to suit your needs.
