Repository Index

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

• gbdt
• gbrt
• gbm
• distributed-systems

A game theoretic approach to explain the output of any machine learning model.

• interpretability
• machine-learning
• deep-learning
• gradient-boosting

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

• gbdt
• gbm
• machine-learning
• data-mining

Machine learning, in numpy

• machine-learning
• neural-networks
• topic-modeling
• gaussian-mixture-models

An elegant PyTorch deep reinforcement learning library.

• pytorch
• policy-gradient
• dqn
• double-dqn

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

• wandb
• reinforcement-learning
• pytorch
• python

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

• machine-learning
• python
• data-science
• automl

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

• yolov9

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

• machine-learning
• decision-trees
• gradient-boosting
• gbm

Pytorch implementation of convolutional neural network visualization techniques

• grad-cam
• cam
• pytorch
• guided-backpropagation

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

• ctr
• click-through-rate
• deep-learning
• factorization-machines

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

• h2o
• machine-learning
• data-science
• deep-learning

Fit interpretable models. Explain blackbox machine learning.

• machine-learning
• interpretability
• gradient-boosting
• blackbox

Gorgonia is a library that helps facilitate machine learning in Go.

• machine-learning
• artificial-intelligence
• neural-network
• computation-graph

Natural Gradient Boosting for Probabilistic Prediction

• machine-learning
• gradient-boosting
• natural-gradients
• uncertainty-estimation

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).

Scalable, event-driven, deep-learning-friendly backtesting library

• reinforcement-learning
• deep-reinforcement-learning
• gym-environment
• openai-gym

This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

• python
• machine-learning
• random-forest
• tensorflow

Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux

• deep-learning
• deep-neural-networks
• gradient-descent
• automatic-differentiation

Ruby Scoring API for PMML

• ruby-gem
• pmml
• random-forest
• classification

Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.