Search Results for "repository" - Page 32
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Showing 1010 open source projects for "repository"
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Deep-Learning-with-PyTorch-Tutorials
Deep Learning and PyTorch Introduction Video Tutorial with Source Code
...The repository is designed for learners who want to connect deep learning concepts with executable examples. Overall, it is a structured PyTorch practice resource for beginners and early deep learning practitioners. -
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Image Quality Assessment
Convolutional Neural Networks to predict aesthetic quality of images
...Instead of relying on simple image statistics, the system learns patterns that correlate with human judgments about image aesthetics and technical quality. The repository includes code for training models, performing inference, and evaluating predicted scores against labeled datasets. It also provides utilities for image preprocessing and data management that help prepare datasets for training deep learning models. -
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gitfs
Version controlled file system
...You can mount a remote repository's branch locally, and any subsequent changes made to the files will be automatically committed to the remote. gitfs was developed by the awesome engineering team at Presslabs, a Managed WordPress Hosting provider. gitfs was designed to bring the full powers of git to everyone, no matter how little they know about versioning. A user can mount any repository and all their changes will be automatically converted into commits. gitfs will also expose the history of the branch you're currently working on by simulating snapshots of every commit. gitfs is useful in places where you want to keep track of all your files, but at the same time you don't have the possibility of organizing everything into commits yourself. ... -
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StarsAndClown
Github Star Gathering Treatment List
StarsAndClown is a repository by the same maintainer that seems intended as a lighthearted “ranking / listing” project, possibly gathering interesting or amusing GitHub repositories, trending topics, or community “stars” — perhaps with a humorous or satirical twist given the name. The concept suggests a curated (or semi-automated) list of GitHub repos worth noting: whether because of popularity, novelty, or community interest — giving “people who eat grapes” (i.e. spectators) a way to enjoy and laugh along with the broader open-source ecosystem. ... -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
...The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. Although the GitHub repository has been archived and is read-only, it is still a valuable snapshot of early, hands-on teaching material for scikit-learn and machine learning in Python. -
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Texar
Toolkit for Machine Learning, Natural Language Processing
...The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this repository is maintained by Petuum Open Source. Two Versions, (Mostly) Same Interfaces. Texar-TensorFlow (this repo) and Texar-PyTorch have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Rich Pre-trained Models, Rich Usage with Uniform Interfaces. BERT, GPT2, XLNet, etc, for encoding, classification, generation, and composing complex models with other Texar components! -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
youtube-8m is Google’s open source starter code and reference implementation for training and evaluating machine learning models on the YouTube-8M dataset, one of the largest video understanding datasets publicly released. The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. ... -
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gpt2-client
Easy-to-use TensorFlow Wrapper for GPT-2 117M, 345M, 774M, etc.
...The model has 4 versions - 124M, 345M, 774M, and 1558M - that differ in terms of the amount of training data fed to it and the number of parameters they contain. Finally, gpt2-client is a wrapper around the original gpt-2 repository that features the same functionality but with more accessiblity, comprehensibility, and utilty. You can play around with all four GPT-2 models in less than five lines of code. Install client via pip. The generation options are highly flexible. You can mix and match based on what kind of text you need generated, be it multiple chunks or one at a time with prompts. -
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Jarvis
Personal Assistant for Linux and macOS
...He can talk to you if you enable his voice. He can tell you the weather, he can find restaurants and other places near you. He can do some great stuff for you. In order to start Jarvis just clone this repository and run python installer. Run Jarvis from anywhere by command jarvis. You can start by typing help within the Jarvis command line to check what Jarvis can do for you. Plugins may be modified using the decorators @alias, @require and @complete. These special decorators may be used in any order or several times.Not all plugins are compatible with every system. ... -
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DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
...Unlike traditional discrete voxel grids or meshes, DeepSDF encodes shapes as continuous neural representations that can be smoothly interpolated and used for reconstruction, generation, and analysis. The repository provides complete tooling for preprocessing mesh datasets (e.g., ShapeNet), training DeepSDF models, reconstructing meshes from learned latent codes, and quantitatively evaluating results with metrics such as Chamfer Distance and Earth Mover’s Distance. -
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Dive-into-DL-TensorFlow2.0
Dive into Deep Learning
...In addition, this project also refers to the project Dive-into-DL-PyTorch , which refactored PyTorch in the Chinese version of this book, and I would like to express my gratitude here. This repository mainly contains two folders, code and docs (plus some data stored in data). The code folder is the relevant jupyter notebook code for each chapter (based on TensorFlow2); the docs folder is the relevant content in the book. -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
...The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and logistic regression to more complex techniques such as decision trees, support vector machines, clustering methods, and neural networks. Because the code avoids external machine learning libraries, it exposes the full logic behind model training, optimization, and prediction processes. ... -
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BlackPack
Graphical RPM package builder
BlackPack is a utility to aid in creating installable RedHat packages (.rpm). The goal behind the project is to make developing for RedHat based Linux distributions more appealing, with an easy to use interface for packaging applications, artwork, media, themes, etc. -
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Python Patterns
A collection of design patterns/idioms in Python
Python-Patterns is a repository collecting implementations of many classical design patterns and idioms, written in Python. It serves as an educational resource: showing how to implement creational, structural, behavioral, testability, and other patterns in a Pythonic style (or sometimes less so), illustrating trade-offs, different styles, and use cases. It’s intended for learners or developers interested in software architecture or design, rather than as a production library. ... -
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BlockCheck
Russian ISP blocking type checker
...It runs tests that help distinguish different blocking methods, such as DNS manipulation, IP blocking, HTTP blocking, TCP reset behavior, and deeper filtering patterns. The repository is now archived and marked as not currently working, so it should be treated as a historical and diagnostic reference rather than a maintained utility. Its value is strongest for people studying network censorship, ISP filtering behavior, and the technical mechanics of access restriction. Because test results could be affected by circumvention tools, the project warns users not to run it while bypass systems are enabled. -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
...The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. The project provides TensorFlow and Sonnet-based implementations, pretrained checkpoints, and example scripts for evaluating or fine-tuning models. ... -
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InferSent
InferSent sentence embeddings
...Trained on large NLI datasets, the embeddings generalize across tasks like sentiment analysis, entailment, paraphrase detection, and semantic similarity with simple linear classifiers. The repository provides pretrained vectors, training scripts, and clear examples for evaluating transfer on a wide suite of benchmarks. Because the encoder is compact and language-agnostic at the interface level, it’s easy to drop into production pipelines that need robust semantic features. InferSent helped popularize the idea that supervised objectives (like NLI) can yield strong general-purpose sentence encoders, and it remains a reliable baseline against which to compare newer models. -
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Azure Machine Learning Python SDK
Python notebooks with ML and deep learning examples
Azure Machine Learning Python SDK is a curated repository of Python-based Jupyter notebooks that demonstrate how to develop, train, evaluate, and deploy machine learning and deep learning models using the Azure Machine Learning Python SDK. The content spans a wide range of real-world tasks — from foundational quickstarts that teach users how to configure an Azure ML workspace and connect to compute resources, to advanced tutorials on using pipelines, automated machine learning, and dataset handling. ... -
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Coursebook
Introductory Systems Programming Textbook for University of Illinois
Welcome to the systems programming coursebook! This repository houses a high-quality, open-source introductory systems programming textbook used by the CS 341: System Programming course at the University of Illinois at Urbana-Champaign The book assumes that you have taken a programming language course and are familiar with assembly instructions. All of the code and instruction will be in C, as it is the de-facto language of the Linux Kernel. -
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AIAlpha
Use unsupervised and supervised learning to predict stocks
...It also demonstrates how models can be evaluated through backtesting frameworks that simulate how a strategy would perform using historical market conditions. By combining financial analytics with machine learning algorithms, the repository illustrates the process of building data-driven investment strategies. -
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pytorch-examples
Simple examples to introduce PyTorch
...The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. By emphasizing readable code, the repository helps users understand how PyTorch’s imperative programming style enables flexible model development. It also serves as a quick reference for common patterns and techniques used in deep learning workflows. The project aligns with PyTorch’s philosophy of combining usability with performance and flexibility. Overall, pytorch-examples is an essential learning resource for anyone working with PyTorch. -
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Daylight-Linux official repository. Daylight linux is an operating system for PC (amd 64 live ) and for Raspberry Pi. (all version from Zero to 4.)
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. ... -
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RefineNet
RefineNet: Multi-Path Refinement Networks
...It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. While this codebase is specific to MATLAB and MatConvNet, a PyTorch implementation and lighter-weight variants are also available from the community. -
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MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning
MAML-Pytorch is a PyTorch implementation of Model-Agnostic Meta-Learning for supervised learning experiments. It focuses on reproducing and exploring the MAML approach for few-shot learning research. The repository supports MiniImagenet and Omniglot, two common benchmark datasets for meta-learning experiments. It includes separate training scripts, dataset loaders, learner components, and meta-learning logic. The project also notes that MAML can be difficult to train and presents the implementation as a practical starting point for research. ...