Machine learning, in numpy
- machine-learning
- neural-networks
- topic-modeling
- gaussian-mixture-models
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Machine learning, in numpy
An elegant PyTorch deep reinforcement learning library.
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
TranslatedSimple Reinforcement learning tutorials, 莫烦Python (Mofan Python, "don't be annoyed Python") Chinese AI teaching
Pytorch implementation of convolutional neural network visualization techniques
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
A specialized Claude Code workspace for creating long-form, SEO-optimized blog content for any business. This system helps you research, write, analyze, and optimize content that ranks well and serves your target audience.
A collection of research papers on decision, classification and regression trees with implementations.
Optax is a gradient processing and optimization library for JAX.
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
A curated list of data mining papers about fraud detection.
A curated list of gradient boosting research papers with implementations.
Scalable, event-driven, deep-learning-friendly backtesting library
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.
A curated list of Monte Carlo tree search papers with implementations.
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
🎯 A comprehensive gradient-free optimization framework written in Python
Agent skill that rebuilds research paper figures as editable Figma vectors — native text, curves and gradients, with SVG/PDF export.
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