pj-mathematician

pj-mathematician's Stars

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook47k3056625.6k

• AntonOsika/gpt-engineer

  Specify what you want it to build, the AI asks for clarification, and then builds it.

  Language:Python46.8k4853777.6k

• gradio-app/gradio

  Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

  Language:Python32.5k1714.8k2.4k

• RVC-Project/Retrieval-based-Voice-Conversion-WebUI

  Easily train a good VC model with voice data <= 10 mins!

  Language:Python23.5k1711.6k3.5k

• HarisIqbal88/PlotNeuralNet

  Latex code for making neural networks diagrams

  Language:TeX21.9k2271222.9k

• facefusion/facefusion

  Industry leading face manipulation platform

  Language:Python18.6k1784382.8k

• unifyai/ivy

  The Unified AI Framework

  Language:Python14k7016.9k5.9k

• Netflix/conductor

  Conductor is a microservices orchestration engine.

  Language:Java12.8k5521.6k2.3k

• NielsRogge/Transformers-Tutorials

  This repository contains demos I made with the Transformers library by HuggingFace.

  Language:Jupyter Notebook9.2k1374451.4k

• autogluon/autogluon

  Fast and Accurate ML in 3 Lines of Code

  Language:Python7.8k1001.4k910

• roboflow/notebooks

  Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

  Language:Jupyter Notebook5.3k78134816

• Giskard-AI/giskard

  🐢 Open-Source Evaluation & Testing for ML models & LLMs

  Language:Python4k33447254

• Nixtla/statsforecast

  Lightning ⚡️ fast forecasting with statistical and econometric models.

  Language:Python3.9k39333273

• Nixtla/neuralforecast

  Scalable and user friendly neural :brain: forecasting algorithms.

  Language:Python3k35537342

• microsoft/Graphormer

  Graphormer is a general-purpose deep learning backbone for molecular modeling.

  Language:Python2.1k31152334

• appium/java-client

  Java language binding for writing Appium Tests, conforms to W3C WebDriver Protocol

  Language:Java1.2k1601.2k756

• LambdaLabsML/examples

  Deep Learning Examples

  Language:Jupyter Notebook8041458103

• booknlp/booknlp

  BookNLP, a natural language processing pipeline for books

  Language:Python785232492

• InboxSDK/InboxSDK

  The InboxSDK lets you build apps for Gmail.

  Language:TypeScript7412513250

• kharrigian/mental-health-datasets

  An evolving list of electronic media data sets used to model mental-health status.

  Language:Python37814372

• dbamman/book-nlp

  Natural language processing pipeline for book-length documents (archival Java version; for current Python version, see: https://github.com/booknlp/booknlp)

  Language:Java30933649

• brucewlee/lingfeat

  [EMNLP 2021] LingFeat - A Comprehensive Linguistic Features Extraction ToolKit for Readability Assessment

  Language:Python1201916

• brucewlee/lftk

  [BEA @ ACL 2023] General-purpose tool for linguistic features extraction; Tested on readability assessment, essay scoring, fake news detection, hate speech detection, etc.

  Language:Python1092422

• Coding-and-Crypto/Rust-Solana-Tutorial

  Examples from Rust Solana Tutorial on YouTube!

  Language:TypeScript1060763

• metal3d/keras-video-generators

  Keras generators to generate sequences from videos as input

  Language:Jupyter Notebook9984332

• X-rayLaser/pytorch-handwriting-synthesis-toolkit

  Handwriting generation and handwriting synthesis as described in Alex Graves's paper https://arxiv.org/abs/1308.0850. Pytorch implementation.

  Language:Python6931012

• aaryan2134/JPMC-Quant-Challenge-2022

  J.P. Morgan Quant Challenge 2022 Questions

  341218

• PriyanshuSharma23/Express-TS-starter

  Language:TypeScript5

• finnecoiitg/Derivative-Modelling---JPMC-Quant-Challenge-2022

  Derivative pricing models are techniques used by investors to try to find an objective measure of a derivative's true value. This is then compared to its actual market price to see whether it represents a worthy investment. Each model takes into account different known factors that affect the derivative.

  Language:Jupyter Notebook4104

• Prasann2004/BB84_Cryptography_implementation_for_transmiting_one_bit

  BB84 is a cryptography protocol used for encoding and decoding messages. It was developed by Charles Bennett and Gilles Brassard in 1984. More description of this protocol is given on Wikipedia page' https://en.wikipedia.org/wiki/BB84#:~:text=BB84%20is%20a%20quantum%20key,the%20first%20quantum%20cryptography%20protocol.'

  Language:Python2100