Search Results for "machine learning platform" - Page 2
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Showing 62 open source projects for "machine learning platform"
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sadsa
SADSA (Software Application for Data Science and Analytics)
SADSA (Software Application for Data Science and Analytics) is a Python-based desktop application designed to simplify statistical analysis, machine learning, and data visualization for students, researchers, and data professionals. Built using Python for the GUI, SADSA provides a menu-driven interface for handling datasets, applying transformations, running advanced statistical tests, machine learning algorithms, and generating insightful plots — all without writing code. -
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SMILI
Scientific Visualisation Made Easy
The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with... -
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Algorithm Visualizer
Interactive Online Platform that Visualizes Algorithms from Code
Hacker Scripts is a light-hearted collection of small automation and demo scripts that solve amusing everyday tasks or illustrate quick integrations with external services. The repo collects short programs (originally a set of shell and Ruby scripts) and many community contributed ports in other languages to show “how you might automate X” — for example sending a quick SMS, firing off an email, or triggering a coffee maker — with examples and scheduling snippets included. The README explains... -
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SciMLBenchmarks.jl
Benchmarks for scientific machine learning (SciML) software
SciMLBenchmarks.jl holds webpages, pdfs, and notebooks showing the benchmarks for the SciML Scientific Machine Learning Software ecosystem. -
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Kinetic.jl
Universal modeling and simulation of fluid mechanics upon ML
Kinetic is a computational fluid dynamics toolbox written in Julia. It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs. -
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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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DiffEqOperators.jl
Linear operators for discretizations of differential equations
DiffEqOperators.jl is a package for finite difference discretization of partial differential equations. It allows building lazy operators for high order non-uniform finite differences in an arbitrary number of dimensions, including vector calculus operators. For the operators, both centered and upwind operators are provided, for domains of any dimension, arbitrarily spaced grids, and for any order of accuracy. The cases of 1, 2, and 3 dimensions with an evenly spaced grid are optimized with... -
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PyNanoLab
data analysis and Visualization with matplotlib
PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI. -
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Augmentor.jl
A fast image augmentation library in Julia for machine learning
A fast library for increasing the number of training images by applying various transformations. Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a... -
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DataStation Community Edition
App to easily query, script, and visualize data from every database
DataStation is an open-source data IDE for developers. It allows you to easily build graphs and tables with data pulled from SQL databases, logging databases, metrics databases, HTTP servers, and all kinds of text and binary files. Need to join or munge data? Write embedded scripts as needed in languages like Python, JavaScript, R or SQL. All in one application. Build reports with graphs, charts and tables. Script against data. Cross-platform: Windows, macOS, and Linux. Easily fetch your... -
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SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML)
SciMLTutorials.jl holds PDFs, webpages, and interactive Jupyter notebooks showing how to utilize the software in the SciML Scientific Machine Learning ecosystem. This set of tutorials was made to complement the documentation and the devdocs by providing practical examples of the concepts. For more details, please consult the docs. To view the SciML Tutorials, go to tutorials.sciml.ai. By default, this will lead to the latest tagged version of the tutorials -
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Chess.jl
Julia chess programming library
...There are functions for creating and manipulating chess games, chess positions and sets of squares on the board, for reading and writing chess games in the popular PGN format (including support for comments and variations), for creating opening trees, and for interacting with UCI chess engines. The library was designed for the purpose of doing machine learning experiments in computer chess, but it should also be suitable for most other types of chess software. -
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MLDataUtils.jl
Utility package for generating, loading, and processing ML datasets
This package is designed to be the end-user facing front-end to all the data related functionality that is spread out across the JuliaML ecosystem. Most of the following sub-categories are covered by a single back-end package that is specialized on that specific problem. Consequently, if one of the following topics is of special interest to you, make sure to check out the corresponding documentation of that package. -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. -
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CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
In machine learning, a classifier assigns a class label to a data point. For example, an image classifier produces a class label (e.g, bird, plane) for what objects exist within an image. A convolutional neural network, or CNN for short, is a type of classifier, which excels at solving this problem! A CNN is a neural network: an algorithm used to recognize patterns in data. -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. ... -
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Spectral Python
A python module for hyperspectral image processing
Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more. -
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Inviwo
Interactive Visualization Workshop
...Built in C++ with a modular, extensible architecture, Inviwo combines a visual editor (for creating data pipelines) with a powerful runtime engine that supports real-time rendering, interaction, and GPU-accelerated processing. It’s widely used in scientific domains for building and sharing visualizations of complex data such as medical imaging, simulations, and machine learning models. The platform supports both novice users through its graphical interface and advanced users through scripting and plugin development. -
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OpenFace
A state-of-the-art facial behavior analysis toolkit
OpenFace is an advanced facial behavior analysis toolkit intended for computer vision and machine learning researchers, those in the affective computing community, and those who are simply interested in creating interactive applications based on facial behavior analysis. The OpenFace toolkit is capable of performing several complex facial analysis tasks, including facial landmark detection, eye-gaze estimation, head pose estimation and facial action unit recognition. -
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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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TensorFlow.jl
A Julia wrapper for TensorFlow
A wrapper around TensorFlow, a popular open-source machine learning framework from Google. -
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GMOL
A tool for 3D genome structure visualization
...This software was built upon the pre-existing Jmol package by Prof. Cheng's group. The software is developed in Prof. Jianlin Cheng's Bioinformatics, Data Mining and Machine Learning Laboratory in the Computer Science Department at the University of Missouri - Columbia, USA. The project is supported by the National Science Foundation (grant no. DBI1149224). If you use GMOL in your research, please cite: Nowotny, Jackson, Avery Wells, Oluwatosin Oluwadare, Lingfei Xu, Renzhi Cao, Tuan Trieu, Chenfeng He, and Jianlin Cheng. ... -
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Please see https://github.com/BRIO-lab/Joint-Track-Machine-Learning for the latest version of the JointTrack/JointTrack Auto lineage.
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Merlin.jl
Deep Learning for Julia
Merlin is a deep learning framework written in Julia. It aims to provide a fast, flexible and compact deep learning library for machine learning. Merlin is tested against Julia 1.0 on Linux, OS X, and Windows (x64). -
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PetaVision
Accelerated Open-Source Neuromorphic Computing
///////// NOTICE We have migrated to GitHub: https://petavision.github.io To clone an updated repository of the PetaVision codebase, please go to our page on GitHub. ///////// PetaVision is an open source, object oriented neural simulation toolbox optimized for high-performance multi-core, multi-node computer architectures. PetaVision is intended for computational neuroscientists who seek to apply neuromorphic models to hard signal processing problems; both to improve on the...