Showing 15 open source projects for "network visualization"

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  • 1
    Awesome Network Analysis

    Awesome Network Analysis

    A curated list of awesome network analysis resources

    awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data.
    Downloads: 0 This Week
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  • 2
    TensorFlow.js

    TensorFlow.js

    TensorFlow.js is a library for machine learning in JavaScript

    TensorFlow.js is a library for machine learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Retrain pre-existing ML models using your own data. Build and train models directly in JavaScript using flexible and intuitive APIs. Tensors are the core datastructure of TensorFlow.js They are a generalization of vectors and matrices to...
    Downloads: 2 This Week
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  • 3
    Penzai

    Penzai

    A JAX research toolkit to build, edit, & visualize neural networks

    Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained...
    Downloads: 0 This Week
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  • 4
    Map of GitHub

    Map of GitHub

    Inspirational Mapping

    ...The project processes GitHub account metadata and GPS or location information (when available) to plot users’ locations and draw connections between communities, resulting in an exploratory visualization where density and network effects become instantly visible. Users can zoom in to explore hotspots like major metropolitan areas or zoom out to see global patterns of activity, making it both a data analytics tool and a piece of data art. The map highlights where developers live, where projects originate, and how open-source participation varies around the world, offering insights into the geography of technology culture and innovation.
    Downloads: 3 This Week
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  • 5
    Neural Network Visualization

    Neural Network Visualization

    Project for processing neural networks and rendering to gain insights

    nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. ...
    Downloads: 1 This Week
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  • 6
    BizCharts

    BizCharts

    Powerful data visualization library based on G2 and React

    BizCharts is Alibaba's general charting component library, dedicated to creating efficient, professional and convenient data visualization solutions in the middle and backend of enterprises. Based on the React charting library packaged by G2 and G2Plot, it has experienced three years of baptism in Alibaba's complex business scenarios. In terms of convenience, ease of use, and richness, it satisfies the business implementation of conventional charts and highly customized charts. After years...
    Downloads: 0 This Week
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  • 7
    TensorNetwork

    TensorNetwork

    A library for easy and efficient manipulation of tensor networks

    ...Tutorials and visualization helpers make it easier to understand how network topology affects expressive power and computational cost.
    Downloads: 0 This Week
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  • 8
    textures.js

    textures.js

    SVG patterns for data visualization

    Textures.js is a JavaScript library for creating SVG patterns. Made on top of d3.js, it is designed for data visualization. Import textures.js from NPM. You can also use textures.js in your HTML page with a <script> tag by downloading textures.js to a local folder or by using the Unpkg CDN network. Textures.js can be used alongside d3.
    Downloads: 0 This Week
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  • 9
    Lucid

    Lucid

    A collection of infrastructure and tools for research

    ...Clone the repository and find them in the notebooks subfolder. You will need to run a local instance of the Jupyter notebook environment to execute them. Feature visualization answers questions about what a network, or parts of a network, are looking for by generating examples.
    Downloads: 0 This Week
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  • 10
    TFLearn

    TFLearn

    Deep learning library featuring a higher-level API for TensorFlow

    ...Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. ...
    Downloads: 0 This Week
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  • 11
    The Neural Process Family

    The Neural Process Family

    This repository contains notebook implementations

    Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from...
    Downloads: 1 This Week
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  • 12
    captcha_break

    captcha_break

    Identification codes

    This project will use Keras to build a deep convolutional neural network to identify the captcha verification code. It is recommended to use a graphics card to run the project. The following visualization codes are jupyter notebookall done in . If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. ...
    Downloads: 0 This Week
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  • 13
    TenorSpace.js

    TenorSpace.js

    Neural network 3D visualization framework

    TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information.
    Downloads: 0 This Week
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  • 14
    DeepDream

    DeepDream

    This repository contains IPython Notebook with sample code

    DeepDream is a small, educational repository that accompanies Google’s original “Inceptionism” blog post by providing a runnable IPython/Jupyter notebook that demonstrates how to “dream” through a convolutional neural network. The notebook shows how to take a trained vision model and iteratively amplify patterns the network detects, producing the hallmark surreal, hallucinatory visuals. It walks through loading a pretrained network, selecting layers and channels to maximize, computing gradients with respect to the input image, and applying multi-scale “octave” processing to reveal fine and coarse patterns. ...
    Downloads: 0 This Week
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  • 15
    The aim of GUINNEA (Graphical User Interfaced Neural Network Architecture) is to develop a comfortable and high-featured neural net simulator which is highly configurable and flexible. It will support many neural nets and visualization features for those
    Downloads: 0 This Week
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