pverghese

pverghese's Stars

• judofyr/spice

  Fine-grained parallelism with sub-nanosecond overhead in Zig

  Language:Zig6318

• Mozilla-Ocho/llamafile

  Distribute and run LLMs with a single file.

  Language:C++19k968

• Omniplex-ai/omniplex

  Open-Source Perplexity

  Language:TypeScript790125

• uiua-lang/uiua

  A stack-based array programming language

  Language:Rust1.5k108

• pal1000/Realtek-UAD-generic

  An unofficial package of generic Realtek Universal Audio Driver made from parts of various OEM specific Reatek Universal Audio drivers intended to work on legacy systems lacking OEM UAD support.

  Language:Batchfile1.1k41

• nichtdax/awesome-totally-open-chatgpt

  A list of totally open alternatives to ChatGPT

  4.5k194

• aircrack-ng/rtl8812au

  RTL8812AU/21AU and RTL8814AU driver with monitor mode and frame injection

  Language:C3.5k768

• antimatter15/alpaca.cpp

  Locally run an Instruction-Tuned Chat-Style LLM

  Language:C10.3k912

• fubark/cyber

  Fast and concurrent scripting.

  Language:Zig1.2k40

• mogria/nixpkgs-config

  home configuration files (dotfiles) using home-manager and nix

  Language:Shell271

• betaveros/noulith

  *slaps roof of [programming language]* this bad boy can fit so much [syntax sugar] into it

  Language:Rust1.1k20

• mitchellh/zig-js

  Access the JS host environment from Zig compiled to WebAssembly.

  Language:Zig1977

• buzz-language/buzz

  👨‍🚀 buzz, A small/lightweight statically typed scripting language

  Language:Zig1.2k34

• jinglescode/time-series-forecasting-pytorch

  Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM

  Language:Jupyter Notebook23091

• jiewwantan/XGBoost_stock_prediction

  XGBoost is known to be fast and achieve good prediction results as compared to the regular gradient boosting libraries. This project attempts to predict stock price direction by using the stock's daily data and indicators derived from its daily data as predictors. As such this is a classification problem.

  Language:Jupyter Notebook2411

• lucasrea/StockForecast

  Using machine learning to predict/forecast the future trend of stock prices.

  Language:Jupyter Notebook4435

• wzchen/stock_market_prediction

  Team Buffalox8 predicts directional movement of stock prices.

  Language:Python343178

• abbodi1406/KMS_VL_ALL_AIO

  Smart Activation Script

  Language:Batchfile8.4k1.1k

• hexops/mach

  zig game engine & graphics toolkit

  Language:Zig3.2k153

• tigerbeetle/tigerbeetle

  The financial transactions database designed for mission critical safety and performance.

  Language:Zig9.7k481

• soxoj/maigret

  🕵️‍♂️ Collect a dossier on a person by username from thousands of sites

  Language:Python10.1k789

• oven-sh/bun

  Incredibly fast JavaScript runtime, bundler, test runner, and package manager – all in one

  Language:Zig73.2k2.7k

• lucidrains/imagen-pytorch

  Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch

  Language:Python8k757

• JerBouma/FinanceToolkit

  Transparent and Efficient Financial Analysis

  Language:Python2.8k340

• JerBouma/FinanceDatabase

  This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.

  Language:Jupyter Notebook3.3k375

• ziglang/zig

  General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.

  Language:Zig33.8k2.5k

• OpenBB-finance/OpenBB

  Investment Research for Everyone, Everywhere.

  Language:Python30.2k2.8k

• Asabeneh/30-Days-Of-React

  30 Days of React challenge is a step by step guide to learn React in 30 days. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

  Language:JavaScript25.4k7.2k

• LSPosed/MagiskOnWSA

  Integrate Magisk root and Google Apps (OpenGApps) into WSA (Windows Subsystem for Android)

  17.3k94.9k

• rmcelreath/stat_rethinking_2022

  Statistical Rethinking course winter 2022

  Language:R4.1k445