adarshj

Deep-CFR

Scalable Implementation of Deep CFR and Single Deep CFR

go-explore

Code for Go-Explore: a New Approach for Hard-Exploration Problems

poker_ai

🤖 An Open Source Texas Hold'em AI

PokerRL

Framework for Multi-Agent Deep Reinforcement Learning in Poker

proactai

Our state-of-the-art machine learning models for CCTV cameras generate real-time alerts for events of interest, eliminating the need for constant monitoring.

proactai.github.io

sinib

:exclamation: This is a read-only mirror of the CRAN R package repository. sinib — Sum of Independent Non-Identical Binomial Random Variables

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

tutorials

A series of machine learning tutorials for Torch7

adarshj/Deep-CFR

Scalable Implementation of Deep CFR and Single Deep CFR

adarshj/go-explore

Code for Go-Explore: a New Approach for Hard-Exploration Problems

adarshj/poker_ai

🤖 An Open Source Texas Hold'em AI

adarshj/PokerRL

Framework for Multi-Agent Deep Reinforcement Learning in Poker

adarshj/proactai

Our state-of-the-art machine learning models for CCTV cameras generate real-time alerts for events of interest, eliminating the need for constant monitoring.

adarshj/proactai.github.io

adarshj/sinib

:exclamation: This is a read-only mirror of the CRAN R package repository. sinib — Sum of Independent Non-Identical Binomial Random Variables

adarshj/stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

adarshj/tutorials

A series of machine learning tutorials for Torch7