withai

CapsuleNet_PokemonClassification

Using Capsule Networks to perform classification of different Pokemon's

Demo-Ranking-protein-protein-interfaces-using-GNN

Docked-protein-Interaction-ranking-using-graph-neural-networks

A deep learning library to rank protein complexes using graph neural networks

Graph-Neural-Networks

A deep learning library for graph data structures

Policy-Gradients-Contextual-Bandit-Problem

The contextual Bandit problem is the intermediate between Simple Bandit problem and the full RL problem. In this experiment we are going to find optimal policy to obtain maximum rewards.

Policy-Gradients-Full-RL-CartPole

This experiment learns the optimal policies by the method of Policy-Gradients in the Full Reinforcement Learning problem in the environment "CartPole" from OpenAI Gym.

Policy-Gradients-Mulit-armed-Bandit-Problem

With the concept of Policy Gradients in Reinforcement Learning we are going find optimal policy for obtaining maximum reward in Multi-armed Bandit Problem

PySyft

Private Deep Learning Client

Q-Learning-Neural-Networks-OpenAI-Gym

With the concept of Q-Learning using Neural Networks in Reinforcement Learning we are going to experiment in the environment "FrozenLake" provided by OpenAI Gym

Q-Table-Learning-OpenAI-Gym

With the concept of Q-Table learning in Reinforcement Learning we are going to experiment in the environment "FrozenLake" provided by OpenAI Gym

withai/Demo-Ranking-protein-protein-interfaces-using-GNN

withai/Policy-Gradients-Contextual-Bandit-Problem

The contextual Bandit problem is the intermediate between Simple Bandit problem and the full RL problem. In this experiment we are going to find optimal policy to obtain maximum rewards.

withai/Docked-protein-Interaction-ranking-using-graph-neural-networks

A deep learning library to rank protein complexes using graph neural networks

withai/CapsuleNet_PokemonClassification

Using Capsule Networks to perform classification of different Pokemon's

withai/Graph-Neural-Networks

A deep learning library for graph data structures

withai/Policy-Gradients-Mulit-armed-Bandit-Problem

With the concept of Policy Gradients in Reinforcement Learning we are going find optimal policy for obtaining maximum reward in Multi-armed Bandit Problem

withai/Q-Table-Learning-OpenAI-Gym

With the concept of Q-Table learning in Reinforcement Learning we are going to experiment in the environment "FrozenLake" provided by OpenAI Gym

withai/autonomous-cars-ND-LaneDetection

Lane detection using computer vision algorithms

withai/Deep_learning_on_databases

Deep Learning models on various databases

withai/Policy-Gradients-Full-RL-CartPole

This experiment learns the optimal policies by the method of Policy-Gradients in the Full Reinforcement Learning problem in the environment "CartPole" from OpenAI Gym.

withai/PyBasset

This project is a PyTorch rewrite of the ML library “Basset”

withai/Q-Learning-Neural-Networks-OpenAI-Gym

With the concept of Q-Learning using Neural Networks in Reinforcement Learning we are going to experiment in the environment "FrozenLake" provided by OpenAI Gym

withai/PySyft

Private Deep Learning Client

withai/cs515-001-s20-JandY-pdfsam

PDFsam, a desktop application to extract pages, split, merge, mix and rotate PDF files

withai/cs515-SOTorrent-clones

withai/Deep-Q-Network-Atari

withai/Model-and-Policy-Networks-Reinforcement-Learning

The Reinforcement Learning problem can be improved in certain circumstances by creating a Model neural network to learn the dynamics of the real environment and learn by experimenting in the Model environment instead of Real environment.

withai/Neural-Image-Caption-Generator

withai/OpenMined

The OpenMined Unity Application

withai/Why-get-more-data

withai/withai.github.io

Build a Jekyll blog in minutes, without touching the command line.