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Showing 11 open source projects for "repository"
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1
Hacker Laws
Laws, theories, principles and patterns useful to developers
Laws, Theories, Principles and Patterns that developers will find useful. There are lots of laws which people discuss when talking about development. This repository is a reference and overview of some of the most common ones. Principles and laws to follow such as: If a program is made up of two parts, part A, which must be executed by a single processor, and part B, which can be parallelised, then we see that adding multiple processors to the system executing the program can only have a limited benefit. ... -
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Megatron-LM
Ongoing research training transformer models at scale
Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. ... -
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nanoGPT
The simplest, fastest repository for training/finetuning models
NanoGPT is a minimalistic yet powerful reimplementation of GPT-style transformers created by Andrej Karpathy for educational and research use. It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare... -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The repository includes implementations, experimental data, and supporting research papers that accompany published studies. Notable works such as Weight Agnostic Neural Networks and Neuroevolution of Self-Interpretable Agents highlight the team’s exploration of how AI can learn more efficiently and transparently. Overall, this repository serves as an open research hub for sharing ideas and advancing the understanding of intelligent systems. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Lucid
A collection of infrastructure and tools for research
...It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. You can run the notebooks on your local machine, too. 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. -
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wav2letter++
Facebook AI research's automatic speech recognition toolkit
First, install Flashlight (using the 0.3 branch is required) with the ASR application. This repository includes recipes to reproduce the following research papers as well as pre-trained models. All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. -
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Data Science at the Command Line
Data science at the command line
Command Line by Jeroen Janssens, published by O’Reilly Media in October 2021. Obtain, scrub, explore, and model data with Unix Power Tools. This repository contains the full text, data, and scripts used in the second edition of the book Data Science at the Command Line by Jeroen Janssens. This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. ... -
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Deep Learning for Medical Applications
Deep Learning Papers on Medical Image Analysis
...It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. The repository may also contain domain-specific modules: loss functions like Dice, focal loss, metrics such as sensitivity/recall/IoU, and visualization utilities for overlaying segmentation masks. -
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DSpace
Open Source "turn-key" institutional repository application
Open Source Digital Asset Management system that enables services for access, provision, stewardship and re-use of digital assets with a focus on educational and research materials For Support, please see: https://wiki.lyrasis.org/display/DSPACE/Support RELEASES: The most recent releases are now distributed via GitHub: https://github.com/DSpace/DSpace/releases MAILING LISTS: Mailing lists have all been moved to Google Groups: https://wiki.lyrasis.org/display/DSPACE/Mailing+Lists -
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10
Bracer
Repository has been moved to: http://dtitov.github.com/bracer
Java library for parsing and evaluating math expressions. Javadoc is available at http://bracer.sourceforge.net/javadoc/ P.S. This library depends on Apache Commons Math, so don't forget to include commons-math-2.2.jar to your project. -
11
Kspace
An institutitutional repository to manage publications
This is an open source institutional repository to manage research publications from conception stage to publication stage. It is designed for storing open access publications and all their corresponding metadata. The software manages peer-reviewed journal publications, confrence abstracts and posters, thesis and dissertations and other academic and research outputs.
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