GitHub-проекты

A general classifier module to allow Bayesian and other types of classifications.

JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby. [Deprecated]

[Deprecated]

Some Machine Learning algorithms, implemented in Ruby. [Deprecated]

Curated list of ML related resources for Ruby.

Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing.

Expose libstemmerc to Ruby. [Deprecated]

Text Retrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby.

Curated link list for practical natural language processing in Ruby.

ESPnet is an end-to-end speech processing toolkit for tasks like speech recognition, translation, and enhancement, using PyTorch and Kaldi-style data processing.

Gym4ReaL is a comprehensive suite of realistic environments designed to support the development and evaluation of RL algorithms that can operate in real-world scenarios. The suite includes a diverse set of tasks exposing RL algorithms to a variety of practical challenges.

DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.

RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.

Application-oriented deep reinforcement learning framework addressing real-world decision problems.

An open source robotics benchmark for meta- and multi-task reinforcement learning

A toolkit for reproducible reinforcement learning research

Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

Modular Deep Reinforcement Learning framework in PyTorch.

Retro Games in Gym

Open-source software for robot simulation, integrated with OpenAI Gym.

ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.

Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.

A library for developing and comparing reinforcement learning algorithms (successor of [gym])(https://github.com/openai/gym).

DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning.

Predicting wine quality.

Predicting closed questions on Stack Overflow.

Merck challenge at Kaggle.

A Kaggle competition: discriminate gender based on handwriting.

Winning solution for the Galaxy Challenge on Kaggle.

Kaggle Dogs vs. Cats - Code for Kaggle Dogs vs. Cats competition.

Code for the Best Buy competition at Kaggle.

Amazon access control challenge.

Predicting job salaries from ads - a Kaggle competition.

Code for Accelerometer Biometric Competition at Kaggle.

Deep learning made easy.

Code for the CIFAR-10 competition at Kaggle, uses cuda-convnet.

Code for the Kaggle acquire valued shoppers challenge.

Kaggle Submission for "Detecting Insults in Social Commentary".

An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle.

> source code for Toxic Comment Classification Challenge.

> source code and experiments results for Santander Value Prediction Challenge.

> source code and experiments results for 2018 Data Science Bowl.

> source code and experiments results for Airbus Ship Detection Challenge.

> source code and experiments results for TGS Salt Identification Challenge.

> source code and experiments results for Google AI Open Images - Object Detection Track.

> source code and experiments results for Home Credit Default Risk.

scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.

lifelines is a complete survival analysis library, written in pure Python