Showing 131 open source projects for "collection"

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  • 1
    CNN Explainer

    CNN Explainer

    Learning Convolutional Neural Networks with Interactive Visualization

    ...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. Neural Networks in general are composed of a collection of neurons that are organized in layers, each with their own learnable weights and biases. Let’s break down a CNN into its basic building blocks. A tensor can be thought of as an n-dimensional matrix. In the CNN above, tensors will be 3-dimensional with the exception of the output layer. A neuron can be thought of as a function that takes in multiple inputs and yields a single output. ...
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  • 2
    NLP-Models-Tensorflow

    NLP-Models-Tensorflow

    Gathers machine learning and Tensorflow deep learning models for NLP

    NLP-Models-Tensorflow is a collection of natural language processing model implementations built using the TensorFlow deep learning framework. The repository provides numerous examples of neural network architectures used in modern NLP research and applications, including text classification, language modeling, machine translation, and sentiment analysis. Each model implementation is designed to illustrate how common NLP architectures operate, such as recurrent neural networks, convolutional models for text processing, and transformer-style attention mechanisms. ...
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  • 3
    Computer Vision Pretrained Models

    Computer Vision Pretrained Models

    A collection of computer vision pre-trained models

    A pre-trained model is a model created by someone else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which...
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  • 4
    Machine Learning Homework

    Machine Learning Homework

    Matlab Coding homework for Machine Learning

    The Machine-Learning-homework repository by user “Ayatans” is a collection of MATLAB code intended to solve or illustrate assignments in machine learning courses. It includes implementations of standard machine learning algorithms (such as regression, classification, etc.), scripts for data loading and preprocessing, and evaluation routines (e.g. accuracy, error metrics). Because it is structured as homework or practice material, the code is likely intended more for didactic use than for production deployment. ...
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    TensorNets

    TensorNets

    High level network definitions with pre-trained weights in TensorFlow

    ...Models are written in tf.contrib.layers, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and end-point. Also, it is easy to deploy and expand a collection of pre-processing and pre-trained weights. Readability. With recent TensorFlow APIs, more factoring and less indenting can be possible. For example, all the inception variants are implemented as about 500 lines of code in TensorNets while 2000+ lines in official TensorFlow models. Reproducibility. You can always reproduce the original results with simple APIs including feature extractions.
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  • 6
    Literature of Deep Learning for Graphs

    Literature of Deep Learning for Graphs

    A comprehensive collection of recent papers on graph deep learning

    ...By structuring the literature into categories, the repository allows researchers to quickly identify influential papers in specific subfields of graph machine learning. The collection includes foundational works that introduced graph convolutional networks as well as more recent research on large-scale graph representation learning and graph generation techniques. The repository is designed as a reference guide for students and researchers who want to explore the rapidly growing field of graph deep learning.
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  • 7
    python-is-cool

    python-is-cool

    Cool Python features for machine learning

    python-is-cool is an educational repository created by AI researcher Chip Huyen that explores lesser-known features and powerful capabilities of the Python programming language. The project serves as a curated guide to Python techniques that many developers may overlook or hesitate to use due to unfamiliarity. It demonstrates practical language features, idiomatic programming patterns, and useful tricks that can make Python code more expressive, efficient, and elegant. Much of the content is...
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  • 8
    AI Cheatsheets

    AI Cheatsheets

    Essential Cheat Sheets for deep learning and machine learning research

    cheatsheets-ai is an open-source repository that collects essential cheat sheets covering many tools and concepts used in machine learning, deep learning, and data science. The project aims to provide quick-reference materials that help engineers, researchers, and students review key techniques and frameworks without reading extensive documentation. It compiles cheat sheets for widely used libraries and technologies such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, and...
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  • 9
    Coursera Machine Learning

    Coursera Machine Learning

    Coursera Machine Learning By Prof. Andrew Ng

    CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models.
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  • 10
    Activity Recognition

    Activity Recognition

    Resources about activity recognition

    This repository is a curated collection of resources, papers, code, and summaries relating to human activity recognition/behavior recognition. It is not a single integrated software package but rather a knowledge base organizing feature extraction methods, deep learning approaches, transfer learning strategies, datasets, and representative research in behavior recognition.
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  • 11
    Girls-In-AI

    Girls-In-AI

    Free learning code series: Xiaobai's introduction to Python

    Girls-In-AI is an educational repository created to encourage women and beginners to learn programming and artificial intelligence through accessible tutorials and practice materials. The project provides a collection of beginner-friendly learning resources covering Python programming, data analysis, machine learning, and deep learning topics. It aims to lower the barrier to entry for people who want to enter the field of artificial intelligence by offering structured learning paths and practical examples. The repository includes Jupyter notebooks, tutorials, and exercises that guide learners through topics such as data processing, machine learning model development, and Kaggle competition practice. ...
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  • 12
    ChainerCV

    ChainerCV

    ChainerCV: a Library for Deep Learning in Computer Vision

    ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer. In ChainerCV, we define the object detection task as a problem of, given an image, bounding box-based localization and categorization of objects. Bounding boxes in an image are represented as a two-dimensional array of shape (R,4), where R is the number of bounding boxes and the second axis corresponds to the coordinates of bounding boxes.
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  • 13
    DS-Take-Home

    DS-Take-Home

    Solution to the book A Collection of Data Science Take-Home Challenge

    DS-Take-Home is a repository that provides practical solutions to a series of real-world data science challenges inspired by the book A Collection of Data Science Take-Home Challenges. The project is designed as a learning resource where aspiring data scientists can study how typical industry-style take-home assignments are solved using data analysis and machine learning techniques. Each challenge is implemented in a separate Jupyter notebook that walks through the process of analyzing datasets, performing exploratory data analysis, building predictive models, and interpreting results. ...
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  • 14
    Python Data Science Tutorials

    Python Data Science Tutorials

    Common data analysis and machine learning tasks using python

    ...The repository is organized by topic so learners can use it as a study roadmap or troubleshooting index. Many links document the earlier Python data science ecosystem, making the project especially valuable as a broad historical resource collection.
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  • 15
    Machine learning Resources

    Machine learning Resources

    Some learning materials and research introduction on machine learning

    Machine learning Resources is an educational GitHub repository that collects resources, tutorials, and implementation examples related to machine learning theory and practice. The project aims to help learners understand machine learning from both conceptual and practical perspectives by combining explanations, research references, and coding examples. It serves as a curated knowledge base that introduces fundamental algorithms and techniques used in modern machine learning systems. The...
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  • 16
    Generative Models

    Generative Models

    Collection of generative models, e.g. GAN, VAE in Pytorch

    This project is a comprehensive open-source collection of implementations of various generative machine learning models designed to help researchers and developers experiment with deep generative techniques. The repository contains practical implementations of well-known architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Restricted Boltzmann Machines, and Helmholtz Machines, implemented primarily using modern deep learning frameworks like PyTorch and TensorFlow. ...
    Downloads: 2 This Week
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  • 17
    The KReator project is a collection of software systems, tools, algorithms and data structures for logic-based knowledge representation. Currently, it includes the software systems KReator and MECore and the library Log4KR: - KReator is an integrated development environment (IDE) for relational probabilistic knowledge representation languages such as Bayesian Logic Programs (BLPs), Markov Logic Networks (MLNs), Relational Maximum Entropy (RME), First-Order Probabilistic Conditional Logic (FO-PCL), and others...
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  • 18
    Swift AI

    Swift AI

    The Swift machine learning library

    Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the usage of Swift AI. Each resides in their own repository and can be built with little or no configuration. ...
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  • 19
    nunn

    nunn

    This is an implementation of a machine learning library in C++17

    nunn is a collection of ML algorithms and related examples written in modern C++17.
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  • 20
    Spark Python Notebooks

    Spark Python Notebooks

    Apache Spark & Python (pySpark) tutorials for Big Data Analysis

    Spark Python Notebooks is a curated collection of example Jupyter notebooks designed to help developers and data engineers learn Apache Spark using Python in an interactive environment. Rather than only providing static code files, this project uses notebooks to teach practical data processing workflows, exposing users to real Spark programming patterns like working with RDDs, DataFrames, and distributed computations.
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  • 21
    PredictionIO

    PredictionIO

    Machine learning server for building predictive applications

    Apache PredictionIO is an open-source machine learning server designed to simplify the process of building and deploying predictive engines. It offers a scalable infrastructure with support for multiple ML algorithms, event data collection, and deployment workflows. Developers can use templates or build custom engines, making it a flexible solution for integrating machine learning into applications.
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  • 22
    KMBOX - Kernel Methods Toolbox

    KMBOX - Kernel Methods Toolbox

    A collection of kernel-based algorithms for Matlab.

    KMBOX is a collection of MATLAB programs that implement kernel-based algorithms, with a focus on regression algorithms and online algorithms. It can be used for nonlinear signal processing and machine learning.
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    Downloads: 2 This Week
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  • 23
    AlgoWiki

    AlgoWiki

    Repository which contains links and resources on different topics

    AlgoWiki is an open educational repository that aggregates a large collection of curated resources covering many areas of computer science, programming, and algorithmic problem solving. The project functions as a structured knowledge index that links to tutorials, articles, courses, and research materials across numerous technical domains. Topics include algorithms, machine learning, artificial intelligence, programming languages, web development, and software engineering practices. ...
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  • 24
    Qix

    Qix

    Machine Learning, Deep Learning, PostgreSQL, Distributed System

    Qix is an open-source repository that aggregates resources related to artificial intelligence, machine learning, and deep learning development. The project is designed as a curated index of useful tools, frameworks, research papers, and learning materials that developers can use to explore the AI ecosystem. It organizes information about libraries, tutorials, datasets, and software tools used in modern AI research and development. The repository helps developers quickly discover relevant...
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  • 25

    Large Scale Optimization Templates

    C++ templates with generic nonlinear optimization algorithms

    Highly tunable, simple to use collection of the templates, containing a set of classes for solving unconstrained large scale nonlinear optimization problems. Currently it contains: -- Limited Memory Quasi Newton (L-BFSG) -- BFSG -- Conjugate Gradient -- Gradient Descent -- Wolf condition Line Search -- Backtracking Line Search -- Exact Golden Search -- Golden Search with Wolf condition We also distribute a set of tests with the library.
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