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ctt062 / TimesFM
Forked from google-research/timesfmTimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.
The most customizable typing website with a minimalistic design and a ton of features. Test yourself in various modes, track your progress and improve your speed.
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Tra…
Production-grade Rust-native trading engine with deterministic event-driven architecture
A framework for efficient model inference with omni-modality models
Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and o…
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
GenAI for Optimization and Decision Intelligence
A high-throughput and memory-efficient inference and serving engine for LLMs
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization
🤱🏻 Turn any webpage into a desktop app with one command.
Fully functional Pokerbot that works on PartyPoker, PokerStars and GGPoker, scraping tables with Open-CV (adaptable via gui) or neural network and making decisions based on a genetic algorithm and …
🚀 A very efficient Texas Holdem GTO solver
GTO Wizard AI entry for MIT's Auction Hold'em Tournament (2024)
Source code for the X Recommendation Algorithm
The fundamental package for scientific computing with Python.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
scikit-learn: machine learning in Python