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Super Resolution datasets and models in Pytorch
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, i…
Backtest trading strategies or train reinforcement learning agents with an event-driven market simulator.
Scripts implementing tennis algorithms + other misc tennis stuff
Avellaneda-Stoikov HFT market making algorithm implementation
A plugin for TensorBoard to customize and export Scalar plots
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Grabs the dominant color or a representative color palette from an image. Uses Python and Pillow.
📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
A game theoretic approach to explain the output of any machine learning model.
Streamlit — A faster way to build and share data apps.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…
Roadmap to becoming an Artificial Intelligence Expert in 2022
The most cited deep learning papers
Learn how to develop, deploy and iterate on production-grade ML applications.
Multiclass Semantic Segmentation using U-NET architecture on aerial drone imagery
This Repository is implementation of majority of Semantic Segmentation Loss Functions
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
PKU-DAIR / open-box
Forked from thomas-young-2013/open-boxTowards Generalized and Efficient Blackbox Optimization System/Package (KDD 2021 & JMLR 2024)
This is a collection of python modules that interact with the Ryze Tello drone.
A list of 99 machine learning projects for anyone interested to learn from coding and building projects
Surrogate modeling and optimization for scientific machine learning (SciML)