GitHub projects

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

A library that makes downloading publicly available neuromorphic datasets a breeze and provides event-based data transformation/augmentation pipelines.

A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.

A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.

A canon of deep learning optimization algorithms.

Adaptive Neural Execution Engine for transformers. Per-token sparse inference with dynamic layer skipping, profiler-based gating, and KV-cache-safe compute reduction.

PyTorch library for creating and training sequence autoencoders in just two lines of code

TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs.

Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. [Deprecated]

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

nnbuilder is a python package that lets you build neural networks in 1 line

Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.

A simple Python framework for building ML/DL workflows and LLM agents faster, with prebuilt trainers, agent templates, logging, configuration management, and much more.

An easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.

This basically to gauge the understanding of Machine Learning Workflow and Regression technique in specific.